JP2017162237A - Generation device, generation method, and generation program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To enable appropriate generation of a graph that connects entities on a network in accordance with a desired association.SOLUTION: A generation device of the present invention includes an acquisition unit and a generation unit. The acquisition unit acquires a first graph comprising a plurality of nodes corresponding to respective entities on a network, and edges connecting the nodes having a desired association. The generation unit generates a second graph having a modified edge between first and second nodes of the plurality of nodes of the first graph acquired by the acquisition unit, the edge being modified in accordance with association of the first and second nodes with the third node.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a generation device, a generation method, and a generation program.

  Conventionally, a technique for performing clustering based on the presence or absence of an edge (connection) between nodes in a graph has been provided. For example, a technique for extracting a cluster structure based on a node in a graph and a set of nodes connected to the node is provided. For example, a technique for performing clustering locally based on the number of nodes common to a set of nodes connected to a certain node in the graph and a set of nodes connected to other nodes is provided.

JP2015-156163A

T. Uno et al. Micro-clustering, “Finding small clusters in large diversity.” CoRR, 2015.

  However, in the above-described conventional technology, it is not always possible to appropriately generate a graph that connects subjects (for example, users) on a network based on a desired relationship. For example, when clustering is performed based on the number of nodes common to a set of nodes connected to a certain node and a set of nodes connected to another node, it is determined whether to perform clustering based on the number of common nodes. It becomes. In such a case, it is difficult to appropriately generate a graph that connects each node based on a desired relationship.

  The present application has been made in view of the above, and an object thereof is to provide a generation device, a generation method, and a generation program that appropriately generate a graph that connects entities on a network based on a desired relationship. To do.

  The generating apparatus according to the present application includes a acquiring unit that acquires a first graph including a plurality of nodes corresponding to each of the main bodies on the network and an edge that connects nodes having a predetermined correspondence relationship, and the acquiring unit The edge between the first node and the second node is changed based on the relationship between the first node, the second node, and the third node among the plurality of nodes in the acquired first graph. And a generating unit for generating the second graph.

  According to one aspect of the embodiment, there is an effect that it is possible to appropriately generate a graph that connects the subjects on the network based on a desired relationship.

FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. FIG. 2 is a diagram illustrating an example of a generation process according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the generation apparatus according to the embodiment. FIG. 4 is a diagram illustrating an example of a transaction information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of the first graph information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of the category information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of the attribute information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of the score information storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of the second graph information storage unit according to the embodiment. FIG. 10 is a flowchart illustrating a generation processing procedure according to the embodiment. FIG. 11 is a diagram illustrating a configuration example of a generation apparatus according to a modification. FIG. 12 is a diagram illustrating an example of the attribute information storage unit according to the modification. FIG. 13 is a diagram illustrating an example of category integration. FIG. 14 is a diagram illustrating an example of category division. FIG. 15 is a diagram illustrating an example of generation of a second graph obtained by integrating a plurality of second graphs. FIG. 16 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the generation device.

  Hereinafter, a generation apparatus, a generation method, and a mode for executing a generation program (hereinafter referred to as “embodiment”) according to the present application will be described in detail with reference to the drawings. Note that the generation device, the generation method, and the generation program according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1-1. Generation process (first graph)]
FIGS. 1 and 2 show a case where a graph in which buyers (users) in electronic commerce (for example, Internet shopping) are nodes and the buyers (nodes) are connected by edges is generated. In FIG. 1, the generation device 100 (see FIG. 3) generates a graph (hereinafter also referred to as “first graph”) in which buyers are connected by an edge based on the commonality of sellers who have purchased goods. Show the case. FIG. 2 shows a case where the generation apparatus 100 generates a graph in which edges between nodes included in the first graph are changed (hereinafter also referred to as “second graph”).

  First, an example of the generation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a generation process according to the embodiment. Specifically, FIG. 1 is a diagram illustrating an example of a first graph generation process.

  In the example shown in FIG. 1, the generation device 100 generates the first graph G11 based on the correspondence between the buyer and the seller (user) in the electronic commerce as shown in the transaction list TL11 (step S1). Each user may be included in one or both of the buyer and the seller. For example, the transaction list TL11 is derived based on the transaction information stored in the transaction information storage unit 121 illustrated in FIG. As shown in the transaction list TL11, buyers (nodes) include buyers (nodes) identified by buyer IDs (node IDs) “N11” to “N14”, “N21” to “N24”, and the like. Hereinafter, the node identified by the buyer ID “N11” is referred to as a node N11. Nodes identified by other buyer IDs are also described in the same manner. For example, a node identified by the buyer ID “N21” is described as a node N21. In the example shown in FIG. 1, only eight nodes N11 to N14 and N21 to N24 in the transaction list TL11 are illustrated for explanation, but the transaction list TL11 includes the number of buyers in electronic commerce, For example, one million nodes may be included.

  Further, as shown in the transaction list TL11, the seller includes sellers identified by seller IDs “SR11” to “SR14”, “SR21” to “SR24”, and the like. Hereinafter, the seller identified by the seller ID “SR11” is referred to as seller SR11. The seller identified by other seller ID is described similarly. For example, the seller identified by the seller ID “SR21” is described as the seller SR21.

  In the transaction list TL11, the numerical values of the grids where the nodes N11 to N14, N21 to N24, etc. shown in the vertical rows intersect with the sellers SR11 to SR14, SR21 to SR24 shown in the horizontal columns are the corresponding buyers. And the number of times the seller has made a business transaction. For example, the numerical value “5” of the square (the upper left square) where the node N11 and the seller SR11 intersect indicates that the buyer corresponding to the node N11 and the seller corresponding to the seller SR11 have made a transaction five times. Show. Further, for example, the numerical value “10” of the grid where the node N22 and the seller SR22 intersect indicates that the buyer corresponding to the node N22 and the seller corresponding to the seller SR22 have made 10 transactions.

  Here, the generation apparatus 100 has a similarity between a set of sellers that are second entities that have made transactions a predetermined number of times or more among nodes corresponding to the buyer that is the first entity and a predetermined threshold value or more. A first graph in which the nodes are connected by an edge is generated. In FIG. 1, the generation apparatus 100 includes a first graph in which nodes having a Jackard coefficient of 0.2 or more between a set of sellers who have made a transaction five or more times are connected by edges among nodes corresponding to buyers. G11 is generated. That is, FIG. 1 shows a case where the predetermined threshold is set to 5 times and the Jackard coefficient is used as an index value indicating the similarity between the sets of sellers.

  The first graph G11 illustrated in FIG. 1 illustrates eight nodes N11 to N14 and N21 to N24. Each of the nodes N11 to N14 and N21 to N24 corresponds to a buyer included in the transaction list TL11. For example, the node N11 in the first graph G11 corresponds to a node (buyer) identified by the buyer ID “N11” in the transaction list TL11. In the example shown in FIG. 1, only eight nodes N11 to N14 and N21 to N24 in the first graph G11 are illustrated for explanation, but the first graph G11 is included in the transaction list TL11. The number of buyers, for example 1 million nodes, may be included.

  In the example of FIG. 1, as shown in the transaction list TL11, the buyer corresponding to the node N11 has purchased the product from the seller corresponding to the seller SR11 (hereinafter also referred to as “number of transactions”) five times. . That is, the buyer corresponding to the node N11 is a buyer whose number of purchases (number of transactions) from the seller corresponding to the seller SR11 is equal to or greater than a predetermined threshold. Further, the buyer corresponding to the node N11 has purchased the product seven times from the seller corresponding to the seller SR13. That is, the buyer corresponding to the node N11 is a buyer whose number of purchases of goods from the seller corresponding to the seller SR13 is equal to or greater than a predetermined threshold. That is, with respect to the buyer corresponding to the node N11, the set of sellers whose number of transactions is equal to or greater than a predetermined threshold (hereinafter also referred to as “seller set of the node N11”) includes the seller SR11, the seller SR13, and the like. Further, as shown in the transaction list TL11, the buyer corresponding to the node N13 has purchased the product six times from the seller corresponding to the seller SR11. That is, the buyer corresponding to the node N13 is a buyer whose number of purchases of goods from the seller corresponding to the seller SR11 is equal to or greater than a predetermined threshold. Further, the buyer corresponding to the node N13 has purchased the product seven times from the seller corresponding to the seller SR14. That is, the buyer corresponding to the node N13 is a buyer whose number of purchases of goods from the seller corresponding to the seller SR14 is equal to or greater than a predetermined threshold. That is, for the buyer corresponding to the node N13, the set of sellers whose number of transactions is equal to or greater than a predetermined threshold (hereinafter, also referred to as “seller set of the node N13”) includes the seller SR11, the seller SR14, and the like. Here, when the Jackard coefficient of the seller set of the node N11 and the seller set of the node N13 is 0.2 or more, the generating apparatus 100 connects the node N11 and the node N13 with an edge. Here, in the above example, a case where the seller included in the seller set of the node N11 is the seller SR11 and the seller SR13, and the seller included in the seller set of the node N13 is the seller SR11 and the seller SR14 is described as an example. In this case, the Jackard coefficient (sim) between the seller sets of the node N11 and the node N13 is “sim = (the product set of the seller set of the node N11 and the seller set of the node N13) / (the seller set of the node N11 and The sum set with the seller set of the node N13) ”. That is, the Jackard coefficient (sim) between the seller sets of the node N11 and the node N13 is “sim = 1/3 (0.33...)”. Specifically, the denominator of the Jackard coefficient (sim) between the seller sets of the node N11 and the node N13 is the number of elements of the union of the seller set of the node N11 and the seller set of the node N13, that is, the sellers SR11 and SR13. , SR14 is “3”. Further, the numerator of the Jackard coefficient (sim) between the seller sets of the node N11 and the node N13 is the number of elements of the product set of the seller set of the node N11 and the seller set of the node N13, that is, “1” of the seller SR11. Become. Thus, the Jackard coefficient (sim) between the seller set of the node N11 and the seller set of the node N13 is “0.33...”, Which is equal to or greater than the threshold value “0.2”. 100 generates a first graph G11 in which the node N11 and the node N13 are connected by an edge. In order to illustrate the calculation of the Jackard coefficient (sim), the seller set of the node N11 and the seller set of the node N13 described above include only two elements, but the seller set and node of the node N11 are illustrated. The seller set of N13 may include a number of elements.

  In the example of FIG. 1, as shown in the transaction list TL11, the buyer corresponding to the node N21 has purchased the product from the seller corresponding to the seller SR22 six times. Therefore, for the buyer corresponding to the node N21, the set of sellers whose number of transactions is equal to or greater than a predetermined threshold (hereinafter also referred to as “seller set of the node N21”) includes the seller SR22 and the like. Further, as shown in the transaction list TL11, the buyer corresponding to the node N22 has purchased the product 10 times from the seller corresponding to the seller SR22. Therefore, for the buyer corresponding to the node N21, the set of sellers whose number of transactions is equal to or greater than a predetermined threshold (hereinafter also referred to as “the seller set of the node N22”) includes the seller SR22 and the like. Further, as shown in the transaction list TL11, the buyer corresponding to the node N24 has purchased the product eight times from the seller corresponding to the seller SR22. Therefore, for the buyer corresponding to the node N21, the set of sellers whose number of transactions is equal to or greater than a predetermined threshold (hereinafter, also referred to as “seller set of the node N24”) includes the seller SR22 and the like. Further, in the example of FIG. 1, it is assumed that the Jackard coefficient (sim) between the seller set of the node N21 and the seller set of the node N22 is 0.2 or more. Further, it is assumed that the Jackard coefficient (sim) between the seller set of the node N21 and the seller set of the node N24 is 0.2 or more. Further, it is assumed that the Jackard coefficient (sim) between the seller set of the node N22 and the seller set of the node N24 is 0.2 or more. Thereby, the generation device 100 generates the first graph G11 in which each of the node N21, the node N22, and the node N24 is connected by an edge. In this way, the generation device 100 generates the first graph G11 as shown in FIG. Note that the generation device 100 may generate the first graph based on various indexes without being limited to the Jackard coefficient. For example, in the generation device 100, among nodes corresponding to the buyer, the number of sellers included in the intersection set of sellers who have made a transaction a predetermined number of times or more becomes a predetermined threshold (for example, “2” or the like). A first graph in which nodes are connected by edges may be generated.

[1-2. Generation process (second graph)]
Next, an example of the generation process according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram illustrating an example of a generation process according to the embodiment. Specifically, FIG. 2 is a diagram illustrating an example of a second graph generation process. 2 illustrates an example in which the generation device 100 generates the first graph by the first graph generation processing illustrated in FIG. 1 or the first graph has already been acquired from the external device. In the example shown in FIG. 2, the category information of the product purchased by the buyer (user) corresponding to each node is used as the node attribute information.

  Further, the example shown in FIG. 2 shows a case where the second hierarchy is used among the categories shown in the category information storage unit 123 shown in FIG. That is, in the example illustrated in FIG. 2, the generation apparatus 100 uses the category information of the products purchased by the nodes N11 to N14 and N21 to N24 as illustrated in the attribute information storage unit 124 as the attribute information. Specifically, the category C11 in the horizontal column shown in the attribute information storage unit 124 is “literature” (see FIG. 6), which is the second hierarchy lower than the category C1 of “book, magazine”, which is the first hierarchy. Correspond. That is, in the example illustrated in FIG. 2, the buyer corresponding to the node N11 indicates that the product belonging to the “literary” category C11 has been purchased five times. Specifically, the category C21 in the horizontal row shown in the attribute information storage unit 124 is “Ladies” (see FIG. 6), which is the second layer below the category C2 of “Fashion” that is the first layer. Correspond. That is, in the example illustrated in FIG. 2, the buyer corresponding to the node N22 indicates that the product belonging to the category “Claim C21” is purchased seven times. For example, the category information of products purchased by each of the nodes N11 to N14 and N21 to N24 is represented by k columns, that is, k-dimensional vectors. In this case, the attribute information of each of the nodes N11 to N14 and N21 to N24 is represented by k columns, that is, k-dimensional vectors.

  In FIG. 2, the production | generation apparatus 100 produces | generates the 2nd graph G12 from the 1st graph G11 (step S2). Here, the generation apparatus 100 connects an edge between one node (hereinafter also referred to as “first node”) and another node (hereinafter also referred to as “second node”) in the first graph G11. A standard score is calculated. Hereinafter, a node other than the first node and the second node for which a score is to be calculated may be a third node. For example, the generation apparatus 100 may determine the similarity between the third node connected to the first node and the first node in the first graph G11, and the second node connected to the second node in the first graph G11 and the second node. A score is calculated based on the similarity with the node. For example, the generating apparatus 100 calculates a score in the combination of the first node and the second node using the following equation (1).

  F (u, v) shown on the left side in the above formula (1) indicates a score in a combination of the node u and the node v. Further, J (U, na) shown on the right side in the above formula (1) indicates the similarity of the attribute information between the node u and the third node (for example, node n). That is, “U” in the above equation (1) indicates a vector of attribute information of the node u (for example, a k-dimensional vector). In addition, “na” in the above formula (1) indicates a vector of attribute information of the node n (for example, a k-dimensional vector). Further, J (V, na) shown on the right side in the above formula (1) indicates the similarity of the attribute information between the node v and the third node (for example, node n). That is, “V” in the above equation (1) indicates a vector of attribute information of the node v (for example, a k-dimensional vector). Here, the function J corresponds to, for example, the Jackard similarity. For example, the value of the function J is calculated as in the following formula (2).

In the above formula (2), for the sake of explanation, J (U, V) is used, and the similarity of the attribute information between the node u and the node v is indicated. That is, “U” in the above equation (2) indicates a vector of attribute information of the node u (eg, a k-dimensional vector), and “V” is a vector of attribute information of the node v (eg, a k-dimensional vector). Vector). For example, u _i (hereinafter also referred to as “first index”) on the right side in the above equation (2) indicates a value corresponding to an i-dimensional element of the attribute information of the node u. For example, u _i is “1” when the number of purchases of the product corresponding to the i-dimensional element of the attribute information of the node u is greater than or equal to a predetermined reference value in the information shown in the attribute information storage unit 124. When the value is less than the predetermined reference value, the value is “0”. Further, for example, v _i (hereinafter also referred to as “second index”) on the right side in the above equation (2) indicates a value corresponding to an i-dimensional element of the attribute information of the node v.

For example, a node u is the node N11, when i is 1, _{u 1} shows one-dimensional element of the attribute information of the node N11, for example, a value corresponding to the category C11. For example, when the node u is the node N11, i is 1, and the predetermined reference value is 3, u ₁ is the one-dimensional attribute information of the node u in the information shown in the attribute information storage unit 124. The number of purchases of the product corresponding to the element, for example, the category C11 is 5, which is equal to or greater than a predetermined reference value, so “1”. The denominator on the right side in the equation (2) indicates the sum of the maximum values of the first index u _i and the second index v _i in each dimension (for example, 1 to k dimensions) of the attribute information. In addition, the numerator on the right side in the above formula (2) indicates the sum of the minimum values of the first index u _i and the second index v _i in each dimension (for example, 1 to k dimensions) of the attribute information. For example, J (U, V) in the above equation (2) is a value between 0 and 1. Note that the value of the number of purchases of the product in the attribute information of the corresponding node u or node v may be used for the first index u _i or the second index v _i .

  From here, it returns to the said Formula (1) and demonstrates. N (u) in the above equation (1) indicates a set of third nodes connected to the node u. Here, the third node connected to the node u is not limited to a node directly connected to the node u by an edge, but is a node indirectly connected to the node u via another third node. Is also included. It should be noted that up to which range nodes are included in N (u) may be appropriately set according to the purpose or the like. For example, N (u) in the above equation (1) may include a node connected to a node connected to the node u by an edge. N (v) in the above formula (1) indicates a set of third nodes connected to the node v. It should be noted that the range of nodes to be included in N (v) may be appropriately set according to the purpose or the like, as in the case of N (u) described above.

  The denominator on the right side in the above equation (1) is the attribute similarity between each third node and node u in the union of the third node connected to node u and the third node connected to node v, and The sum of the attribute similarity values having the maximum value among the attribute similarity values of the third node and the node v is shown. The numerator on the right side in the above formula (1) is the attribute similarity between each third node and node u in the product set of the third node connected to node u and the third node connected to node v. The sum of the attribute similarity values having the smallest value among the attribute similarity values of the third node and the node v is shown. For example, f (u, v) in the above formula (1) has a value of 0 to 1.

  The generation device 100 generates the second graph G12 in which the edge between the first node and the second node is changed based on the score calculated by the above formula (1) and a predetermined threshold value. For example, the generating apparatus 100 cancels an edge connecting nodes in the first graph G11 based on the score calculated by the above formula (1) and a predetermined threshold, or uses a new edge between nodes. The second graph G12 is generated by connecting them.

  For example, assume that the predetermined threshold is 0.5. In this case, for example, when the score calculated by the above formula (1) is 0.5 or more, the generation device 100 connects the first node and the second node corresponding to the score with an edge. For example, when the score of the first node and the second node connected by the edge in the first graph G11 is 0.5 or more, the generation apparatus 100 determines whether the first node corresponding to the score is between the first node and the second node. Maintain connecting edges. In addition, for example, when the score of the first node and the second node that are not connected by the edge in the first graph G11 is 0.5 or more, the generation device 100 includes the first node and the second node corresponding to the score, Connect between the edges. Further, for example, when the score of the first node and the second node connected by the edge in the first graph G11 is less than 0.5, the generation device 100 calculates the first node and the second node corresponding to the score. Release the edge that connects them.

  In the example of FIG. 2, the generation device 100 has a node N11 and a node N12 that are not connected by an edge in the first graph G11, and the score is equal to or greater than the threshold value. Therefore, in the second graph G12, The node N12 is connected by an edge. In the example of FIG. 2, the generation device 100 has a node N11 and a node N13 connected by edges in the first graph G11 having a score of 0.7, which is equal to or higher than the threshold value. An edge connecting N11 and node N13 is maintained. In the example of FIG. 2, the generation device 100 has a node N21 and a node N22 connected by edges in the first graph G11 with a score of 0.2, which is less than the threshold value. And the edge connecting the node N22 is released.

  As described above, the generation apparatus 100 selects the first node based on the first node attribute information and the relationship between the second node attribute information and the third node attribute information among the plurality of nodes in the first graph G11. A second graph G12 in which the edge between the node and the second node is changed is generated. Thereby, the production | generation apparatus 100 can produce | generate the graph which connects the main body on a network based on desired relationship appropriately.

  Further, for example, the generation apparatus 100 may cluster nodes based on a clique (maximum clique) included in the second graph G12. For example, the clique here means a node group in which all nodes included in the clique are all connected. Note that the generation apparatus 100 may cluster nodes using a conventional method such as microclustering as appropriate. For example, the generation apparatus 100 may cluster nodes using the clique calculation process described in Non-Patent Document 1. Further, for example, the generation apparatus 100 may use processing (program) for extracting a clique / maximum clique of a graph, for example, MACE (MAximal Clique Enumerater). Note that the above is an example, and the generation apparatus 100 may cluster nodes by any means.

  For example, the generating apparatus 100 may cluster a node group in which nodes are connected by an edge in the second graph G12 as one cluster. For example, in the generation device 100, the node N11, the node N12, and the node N13 included in the second graph G12 are cliques in which all nodes are connected by edges. For example, the generation device 100 may cluster the node N11, the node N12, and the node N13 included in the second graph G12 as one cluster. For example, the generation device 100 may cluster the node N11, the node N12, and the node N14 included in the second graph G12 as one cluster. In the second graph G12, when the node N13 and the node N14 are connected by an edge, the generation device 100 may cluster the node N11, the node N12, the node N13, and the node N14 as one cluster.

  In the above-described example, the case where the first graph in which nodes corresponding to the buyer are connected by the edge is used is shown. However, the generation apparatus 100 connects the nodes corresponding to various subjects by the edge without being limited to the buyer. The first graph may be used. For example, the generating apparatus 100 may use a first graph in which nodes corresponding to a subject (user) in a predetermined SNS (Social Networking Service) are connected by an edge. For example, the generation apparatus 100 may use a social graph indicating the correlation of the user (human) on the network (Web) as the first graph. That is, the generating apparatus 100 may use a graph indicating the correlation between users on the network as the first graph. Note that the node may include the entire subject (user) included in the social graph, or may include some subject (user) satisfying a predetermined condition. Further, for example, a node may be associated with various objects as well as a user as long as it is an object (subject) in real space. For example, when electronic devices can communicate with each other by various technologies related to so-called IoT (Internet of Things) or the like, for example, each electronic device such as a wearable terminal may be used as one entity, and each electronic device may be associated with a node. Good. In this case, the concept corresponding to the subject (user) may include various action subjects other than humans such as an electronic device used by the user and an electronic device that operates autonomously regardless of an operation by the user. As described above, the generation apparatus 100 may generate the second graph using the first graph in which nodes associated with various objects (subjects) are connected by edges, not limited to the user. . That is, the generating apparatus 100 may generate the second graph using the first graph in which users and electronic devices are mixed. For example, the generating apparatus 100 may generate the second graph using a first graph in which a node corresponding to the user and a node corresponding to the electronic device are connected by an edge. Thus, the generation apparatus 100 may use a first graph in which nodes corresponding to any subject are connected by an edge. That is, the generating apparatus 100 may generate the second graph using any first graph.

[2. Configuration of the generator
Next, the configuration of the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration example of the generation apparatus according to the embodiment. The generation device 100 is an information processing device that generates a second graph based on the first graph. As illustrated in FIG. 3, the generation device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. Note that the generation apparatus 100 may include a display unit that displays various types of information and an input unit that inputs various types of information.

(Communication unit 110)
The communication unit 110 is realized by a NIC or the like, for example. The communication unit 110 is connected to a predetermined network by wire or wireless, and transmits / receives information to / from an external information processing apparatus.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 120 according to the embodiment includes a transaction information storage unit 121, a first graph information storage unit 122, a category information storage unit 123, an attribute information storage unit 124, and a score information storage unit. 125 and a second graph information storage unit 126.

(Transaction information storage unit 121)
The transaction information storage unit 121 according to the embodiment stores transaction information. FIG. 4 is a diagram illustrating an example of a transaction information storage unit according to the embodiment. In the example shown in FIG. 4, transaction log information in electronic commerce is stored as transaction information. For example, the transaction information stored in the transaction information storage unit 121 is used for generating the first graph. As shown in FIG. 4, the transaction information storage unit 121 includes items such as “transaction ID”, “buyer ID (node ID)”, “seller ID”, “product ID”, and “date / time” as transaction information. . Note that the items are not limited to the above, and the transaction information storage unit 121 may have various items depending on the purpose, such as items related to a category to which a traded product belongs and items related to a transaction amount.

  “Transaction ID” indicates identification information for identifying each transaction in the electronic commerce. The “buyer ID (node ID)” indicates identification information for identifying the subject (user) who is the buyer in the transaction identified by the corresponding transaction ID. “Seller ID” indicates identification information for identifying a subject (user) who is a seller in the transaction identified by the corresponding transaction ID. In the example illustrated in FIG. 4, a node ID is used as the buyer ID, but a user ID or shop ID associated with the node ID may be used. The node ID corresponds to the code of each node shown in FIG. For example, the buyer identified by the node ID “N11” corresponds to the node N11 illustrated in FIG. That is, the node identified by the node ID “N11” and the node N11 in FIG. 1 indicate the same node, and the same applies to the other nodes illustrated in FIG. The “product ID” indicates identification information for identifying a product sold and sold in the transaction identified by the corresponding transaction ID. The “date and time” indicates the date and time when the transaction identified by the corresponding transaction ID is established.

  For example, in the example shown in FIG. 4, in the transaction identified by the transaction ID “T11”, the buyer corresponding to the node identified by the buyer ID (node ID) “N11” is identified by the seller ID “SR11”. This indicates that the merchandise identified by the merchandise ID “G11” is purchased from the seller on the date “2016/2/22 13:05”.

(First graph information storage unit 122)
The first graph information storage unit 122 according to the embodiment stores information related to the first graph (hereinafter also referred to as “first graph information” or simply “first graph”). FIG. 5 is a diagram illustrating an example of the first graph information storage unit according to the embodiment. As illustrated in FIG. 5, the first graph information storage unit 122 stores, as first graph information, the presence / absence of an edge connecting nodes corresponding to buyers identified by node IDs (buyer IDs). For example, “1” is stored when there is an edge connecting nodes, and “0” is stored when there is no edge connecting nodes.

  For example, in the example illustrated in FIG. 5, in a region where the row of the node ID “N11” and the column of the node ID “N12” intersect, the node corresponding to the buyer identified by the node ID “N11” and the node ID “ The presence or absence of an edge with the node corresponding to the buyer identified by “N12” is stored. In the example illustrated in FIG. 5, “0” is stored in an area where the row of the node ID “N11” and the column of the node ID “N12” intersect. That is, the edge is not connected between the node corresponding to the buyer identified by the node ID “N11” and the node corresponding to the buyer identified by the node ID “N12”. In the example illustrated in FIG. 5, “1” is stored in an area where the row of the node ID “N11” and the column of the node ID “N13” intersect. That is, the edge is connected between the node corresponding to the buyer identified by the node ID “N11” and the node corresponding to the buyer identified by the node ID “N13”. In this way, the first graph information storage unit 122 indicates that an edge is connected between the nodes corresponding to the area where “1” is stored.

  In the example illustrated in FIG. 5, the row of the node ID “N11” and the column of the node ID “N12” intersect in the region where the row of the node ID “N12” and the column of the node ID “N11” intersect. The same information as the area to be stored is stored. The generation apparatus 100 determines whether the row of the node ID “N11” and the column of the node ID “N12” intersects or the region of the node ID “N12” and the column of the node ID “N11” intersect. Only one of them may be stored. Note that “0” may be stored in an area where the row of the node ID “N11” and the column of the node ID “N11” intersect. The same applies to other node IDs “N12” and the like. In the example shown in FIG. 5, the case of storing in the form of a matrix of node IDs and node IDs is illustrated as an example. However, as long as the first graph information can be stored, it can be stored in any way. Good. For example, the first graph information may be stored in the form of a dictionary that stores a node ID having an edge with respect to each node ID, that is, a list of node IDs linked to each node ID.

(Category information storage unit 123)
The category information storage unit 123 according to the embodiment stores information related to categories (hereinafter also referred to as “category information”). FIG. 6 is a diagram illustrating an example of the category information storage unit according to the embodiment. In the example shown in FIG. 6, information related to a category for classifying a traded product is stored. As illustrated in FIG. 6, the category information storage unit 123 includes items of “first hierarchy”, “second hierarchy”, and “third hierarchy” as category information. The items are not limited to the above, and the category information storage unit 123 may include items such as “fourth hierarchy” and “fifth hierarchy” in accordance with the hierarchical structure of categories.

  The “first hierarchy” includes a “book, magazine” category C1, a “fashion” category C2, and the like. Further, the “second hierarchy” below the “book, magazine” category C1, which is the first hierarchy, includes the “literary” category C11, the “children” category C12, and the like. Further, the “third hierarchy” below the “literary” category C11 which is the second hierarchy includes a “fantasy” category C111, a “non-fiction” category C112, a “history” category C113, and the like. The above category hierarchy is an example, and various category hierarchies may be configured according to the purpose. The category information stored in the category information storage unit 123 may be changed as appropriate. This point will be described later.

(Attribute information storage unit 124)
The attribute information storage unit 124 according to the embodiment stores information related to buyer (user) attributes (hereinafter also referred to as “attribute information”). FIG. 7 is a diagram illustrating an example of the attribute information storage unit according to the embodiment. In the example illustrated in FIG. 7, the attribute information storage unit 124 stores, as attribute information, category information of a product purchased by the buyer (user). As shown in FIG. 7, the attribute information storage unit 124 stores the number of times of purchase for each category of the product purchased by the buyer identified by the node ID (buyer ID) as attribute information.

  For example, in the example shown in FIG. 7, the buyer corresponding to the node N11 has purchased a product belonging to the category “C11” of “literary arts” five times and has never purchased a product belonging to the category “C12” of “children”. Show. Further, for example, in the example illustrated in FIG. 7, the buyer corresponding to the node N12 has purchased a product belonging to the “literary” category C11 four times and purchased a product belonging to the “children” category C12 once. Indicates.

(Score information storage unit 125)
The score information storage unit 125 according to the embodiment stores information related to the score in each node combination (hereinafter also referred to as “score information”). FIG. 8 is a diagram illustrating an example of the score information storage unit according to the embodiment. In the example illustrated in FIG. 8, the score calculated for each combination of the first node and the second node is stored. As shown in FIG. 8, the score information storage unit 125 includes items of “buyer ID (node ID)” and “score” as score information. The “buyer ID (node ID)” includes items of “first node” and “second node”. Note that the items are not limited to the above, and the score information storage unit 125 may have various items according to the purpose.

  “First node” and “second node” indicate a set of nodes for which the corresponding score is calculated. “Score” indicates a score calculated for a corresponding set of nodes.

  For example, the example shown in FIG. 8 indicates that the score calculated for a set of nodes N11 and N12 is “0.8”. Further, for example, in the example illustrated in FIG. 8, the score calculated for a set of nodes of the node N11 and the node N13 is “0.7”. For example, in the example illustrated in FIG. 8, the score calculated for a pair of nodes of the node N21 and the node N22 is “0.2”.

(Second graph information storage unit 126)
The second graph information storage unit 126 according to the embodiment stores information related to the second graph (hereinafter also referred to as “second graph information” or simply “second graph”). FIG. 9 is a diagram illustrating an example of the second graph information storage unit according to the embodiment. As illustrated in FIG. 9, the second graph information storage unit 126 stores, as second graph information, the presence / absence of an edge that connects nodes corresponding to the buyer identified by the node ID (buyer ID). Specifically, the second graph information storage unit 126 stores second graph information generated based on the first graph information stored in the first graph information storage unit 122. For example, “1” is stored when there is an edge connecting nodes, and “0” is stored when there is no edge connecting nodes.

  For example, in the example illustrated in FIG. 9, in a region where the row of the node ID “N11” and the column of the node ID “N12” intersect, the node corresponding to the buyer identified by the node ID “N11” and the node ID “ The presence or absence of an edge with the node corresponding to the buyer identified by “N12” is stored. In the example illustrated in FIG. 9, “1” is stored in an area where the row of the node ID “N11” and the column of the node ID “N12” intersect. That is, the edge is connected between the node corresponding to the buyer identified by the node ID “N11” and the node corresponding to the buyer identified by the node ID “N12”. As described above, the second graph information stored in the second graph information storage unit 126 includes the nodes N11 and N12 whose edges are not connected in the first graph information stored in the first graph information storage unit 122. Indicates that an edge is connected between

  For example, in the example illustrated in FIG. 9, in a region where the row of the node ID “N21” and the column of the node ID “N22” intersect, the node identified by the node ID “N21” and the node ID “ The presence or absence of an edge with the node corresponding to the buyer identified by “N22” is stored. In the example illustrated in FIG. 9, “0” is stored in an area where the row of the node ID “N21” and the column of the node ID “N22” intersect. That is, the edge is not connected between the node corresponding to the buyer identified by the node ID “N21” and the node corresponding to the buyer identified by the node ID “N22”. As described above, the second graph information stored in the second graph information storage unit 126 includes the nodes N21 and N22 whose edges are connected in the first graph information stored in the first graph information storage unit 122. Indicates that the edge between has been released.

  In the example illustrated in FIG. 9, the row of the node ID “N11” and the column of the node ID “N12” intersect in the region where the row of the node ID “N12” and the column of the node ID “N11” intersect. The same information as the area to be stored is stored. The generation apparatus 100 determines whether the row of the node ID “N11” and the column of the node ID “N12” intersects or the region of the node ID “N12” and the column of the node ID “N11” intersect. Only one of them may be stored. Note that “0” may be stored in an area where the row of the node ID “N11” and the column of the node ID “N11” intersect. The same applies to other node IDs “N12” and the like. In the example shown in FIG. 9, the case where the shape is stored in the form of a matrix of node IDs and node IDs is illustrated as an example. However, as long as the second graph information can be stored, it can be stored in any way. Good. For example, the second graph information may be stored in the form of a dictionary that stores a node ID having an edge with respect to each node ID, that is, a list of node IDs linked to each node ID.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is, for example, a controller, and various programs (corresponding to an example of a generation program) stored in a storage device inside the generation device 100 by a CPU, an MPU, or the like. Is implemented by using the RAM as a work area. The control unit 130 is, for example, a controller, and is realized by an integrated circuit such as an ASIC or FPGA.

  As shown in FIG. 3, the control unit 130 includes an acquisition unit 131, a first generation unit 132, a second generation unit 133, and a transmission unit 134, and functions and functions of information processing described below. Realize or execute. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 3, and may be another configuration as long as the information processing described below is performed. Further, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship illustrated in FIG. 3, and may be another connection relationship.

(Acquisition part 131)
The acquisition unit 131 acquires various types of information. The acquisition unit 131 acquires transaction information, first graph information, and the like. For example, the acquisition unit 131 may acquire transaction information from an external device that provides an electronic commerce service. The acquisition unit 131 stores transaction information acquired from an external device that provides an electronic commerce service in the transaction information storage unit 121. For example, the acquisition unit 131 acquires attribute information from the attribute information storage unit 124.

  The acquisition unit 131 acquires first graph information and the like. For example, the acquisition unit 131 acquires a first graph in which nodes corresponding to a first entity satisfying a predetermined condition by a second entity having a predetermined relationship with each first entity are connected by an edge. For example, the acquisition unit 131 acquires a first graph in which nodes corresponding to buyers satisfying a predetermined condition with a set of sellers having a predetermined relationship with each buyer are connected by an edge. For example, the acquisition unit 131 acquires a first graph in which nodes having a similarity equal to or greater than a predetermined threshold among nodes corresponding to the buyer are connected by an edge. To do. For example, the acquisition unit 131 acquires a first graph in which nodes having a Jackard coefficient of 0.2 or more between a set of sellers who have made a transaction five times or more are connected by an edge among nodes corresponding to the buyer. To do. For example, the acquisition unit 131 acquires a first graph in which attribute information is associated with each node. For example, the acquisition unit 131 acquires a first graph in which category information of a product having a predetermined relationship with a user corresponding to each node is associated with each node as attribute information. For example, the acquisition unit 131 acquires a first graph in which category information of a product purchased by a user corresponding to each node is associated with each node as attribute information. For example, the acquisition unit 131 acquires first graph information from the first graph information storage unit 122. For example, the acquisition unit 131 may acquire first graph information from an external device. The acquisition unit 131 may store the first graph information acquired from the external device in the first graph information storage unit 122. Moreover, the acquisition part 131 may acquire the 1st graph with which the attribute information of the user corresponding to each node was matched with each node as attribute information. The acquisition unit 131 acquires a first graph in which attribute information of the first category and attribute information of the second category are associated with each node. These points will be described later.

(First generation unit 132)
The 1st production | generation part 132 produces | generates a 1st graph based on transaction information. For example, the first generation unit 132 generates a first graph in which a set of sellers having a predetermined relationship with each buyer connects nodes corresponding to buyers satisfying a predetermined condition with edges. For example, the first generation unit 132 generates the first graph based on the transaction information stored in the transaction information storage unit 121. In FIG. 1, the 1st production | generation part 132 produces | generates the 1st graph G11 based on a response | compatibility with the buyer and seller (user) in electronic commerce as shown in transaction list TL11. For example, the first generation unit 132 includes, among the nodes corresponding to the buyer, a first graph in which nodes having a similarity between a set of sellers who have made a transaction a predetermined number of times or more are connected by edges. Is generated. In FIG. 1, the 1st production | generation part 132 connected between the nodes from which the Jackard coefficient between the sets of the seller who performed the transaction 5 times or more among the nodes corresponding to a buyer became 0.2 or more with an edge. One graph G11 is generated. For example, the first generation unit 132 generates a first graph G11 in which the node N11 and the node N13 are connected by an edge. For example, the first generation unit 132 generates a first graph G11 in which each of the node N21, the node N22, and the node N24 is connected by an edge.

  Note that the generation device 100 may not include the first generation unit 132 when acquiring the first graph information from the external device.

(Second generation unit 133)
The second generation unit 133 is a generation unit that generates a second graph based on the first graph. For example, the second generation unit 133 selects the first node and the second node based on the relationship between the first node, the second node, and the third node among the plurality of nodes in the first graph acquired by the acquisition unit 131. A second graph in which an edge between two nodes is changed is generated.

  The second generation unit 133 generates the second graph based on the relationship with the third node in which the first node and the second node are connected by an edge. For example, the second generation unit 133 generates the second graph based on the similarity between the first node and the third node and the similarity between the second node and the third node. For example, the second generation unit 133 generates the second graph based on the number of third nodes in which the first node and the second node are connected by an edge.

  The second generation unit 133 generates the second graph based on the attribute information of the first node and the relationship between the attribute information of the second node and the attribute information of the third node. For example, the 2nd generation part 133 generates the 2nd graph based on the 1st graph where the category information of the goods which the user corresponding to each node purchased as attribute information was matched with each node.

  In FIG. 2, the second generation unit 133 generates a second graph G12 from the first graph G11. For example, the second generation unit 133 includes a similarity between the first node and the third node connected to the first node in the first graph G11, and the third node connected to the second node in the first graph G11. A score is calculated based on the similarity with the second node. For example, the second generation unit 133 calculates a score in the combination of the first node and the second node using the above formula (1).

  For example, the second generation unit 133 generates the second graph G12 in which the edge between the first node and the second node is changed based on the score calculated by the above formula (1) and a predetermined threshold value. To do. For example, the second generation unit 133 cancels an edge connecting nodes in the first graph G11 based on the score calculated by the above formula (1) and a predetermined threshold, or creates a new node between nodes. The second graph G12 is generated by connecting the edges.

  For example, in the example of FIG. 2, when the score calculated by the above equation (1) is 0.5 or more, the second generation unit 133 performs an edge between the first node and the second node corresponding to the score. Connect with For example, when the score of the first node and the second node connected by the edge in the first graph G11 is 0.5 or more, the second generation unit 133 calculates the first node and the second node corresponding to the score. Maintain edges that connect between them. In addition, for example, when the score of the first node and the second node that are not connected by the edge in the first graph G11 is 0.5 or more, the second generation unit 133 sets the first node and the second corresponding to the score. Connect nodes with edges. In addition, for example, when the score of the first node and the second node connected by the edge in the first graph G11 is less than 0.5, the second generation unit 133 outputs the first node and the second node corresponding to the score. The edge which connects between is released.

  For example, since the score of the node N11 and the node N12 that are not connected by the edge in the first graph G11 is 0.8 and is equal to or higher than the threshold value, the second generation unit 133 has the node N11 and the node N12 in the second graph G12. Is connected with an edge. Further, for example, the second generation unit 133 has a node N11 and a node N13 connected by edges in the first graph G11 having a score of 0.7, which is equal to or higher than the threshold value. An edge connecting the node N13 is maintained. In addition, for example, the second generation unit 133 has a node N21 and a node N22 connected by edges in the first graph G11 having a score of 0.2 that is less than the threshold. The edge connecting N22 is released.

  In addition, the 2nd production | generation part 133 may perform the following various processes not only in production | generation of said 2nd graph. For example, the second generation unit 133 changes the category corresponding to the attribute information based on the attribute information of nodes belonging to a predetermined cluster included in the second graph. For example, when there are a plurality of attribute information common to nodes belonging to a predetermined cluster included in the second graph, the second generation unit 133 integrates categories corresponding to the attribute information. For example, when there is attribute information common to nodes belonging to each of a plurality of clusters included in the second graph, the second generation unit 133 divides a category corresponding to the attribute information. For example, the second generation unit 133 may generate the second graph based on the first graph in which the attribute information of the user corresponding to each node is associated with each node as the attribute information. Further, for example, the second generation unit 133 is based on the attribute information of the first category of the first node and the relationship between the attribute information of the first category of the second node and the attribute information of the first category of the third node. The second graph of the first category is generated. Further, for example, the second generation unit 133 is based on the attribute information of the second category of the first node and the relationship between the attribute information of the second category of the second node and the attribute information of the second category of the third node. Then, a second graph of the second category is generated. Further, for example, the second generation unit 133 generates a second graph based on the second graph of the first category and the second graph of the second category. These points will be described later.

(Transmitter 134)
The transmission unit 134 transmits various types of information to an external device. For example, the transmission unit 134 may transmit the second graph information generated by the second generation unit 133 to an external information processing apparatus.

[3. Generation process flow)
Next, a generation process procedure performed by the generation apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 10 is a flowchart illustrating a generation processing procedure performed by the generation apparatus 100 according to the embodiment.

  As illustrated in FIG. 10, the acquisition unit 131 of the generation apparatus 100 acquires transaction information (step S101). For example, the acquisition unit 131 acquires transaction information from the transaction information storage unit 121. And the 1st production | generation part 132 of the production | generation apparatus 100 produces | generates a 1st graph based on the transaction information acquired by the acquisition part 131 (step S102). For example, the first generation unit 132 generates the first graph G11.

  Thereafter, the acquisition unit 131 acquires attribute information (step S103). For example, the acquisition unit 131 acquires attribute information from the attribute information storage unit 124. And the 2nd production | generation part 133 of the production | generation apparatus 100 calculates a score for every combination of a node based on the 1st graph produced | generated in step S102, and the attribute information acquired in step S103 (step S104). For example, the second generation unit 133 calculates a score in the combination of the first node and the second node using the above formula (1).

  Then, the second generation unit 133 generates a second graph based on the score calculated in step S104 (step S105). For example, the second generation unit 133 generates the second graph G12. Note that step S104 and step S105 may be repeated. For example, the generating apparatus 100 may calculate a score for each combination of nodes, and repeat the process of releasing edges between the nodes or connecting the nodes with edges.

[4. (Modification)
The generation apparatus 100 according to the above-described embodiment may be implemented in various different forms other than the above-described embodiment. Therefore, in the following, another embodiment of the generation apparatus 100 will be described.

[5. Configuration of generation apparatus having other attribute information]
In the above example, the category information of the product purchased by the buyer is used as the attribute information of the user (buyer). However, the buyer's attribute information is not limited to the category information of the product, but various information is used. Also good. That is, attribute information of various different categories may be used as appropriate for the attribute information. With respect to this point, the configuration of the generation device 100A according to the modification will be described with reference to FIG. FIG. 11 is a diagram illustrating a configuration example of a generation apparatus according to a modification. Note that in the generation device 100A, the same configurations as those of the generation device 100 according to the embodiment are denoted by the same reference numerals, and description thereof is omitted.

  As illustrated in FIG. 11, the generation device 100A is different from the generation device 100 in that the storage unit 120A includes an attribute information storage unit 124A, a score information storage unit 125A, and a second graph information storage unit 126A. In the example illustrated in FIG. 11, the category information storage unit is not illustrated, but the generation apparatus 100A may include a category information storage unit.

(Attribute information storage unit 124A)
The attribute information storage unit 124A according to the modification stores the attribute information of the buyer (user). FIG. 12 is a diagram illustrating an example of the attribute information storage unit according to the modification. In the example shown in FIG. 12, the demographic attribute (demographic attribute) of the buyer is stored as attribute information in the attribute information storage unit 124A. As shown in FIG. 12, the attribute information storage unit 124A stores demographic attributes relating to the sex, age, etc. of the buyer identified by the node ID (buyer ID) as attribute information. The attribute information is not limited to the demographic attribute, and may include various information depending on the purpose. For example, the attribute information may include information regarding psychological attributes (psychographic attributes).

  For example, in the example shown in FIG. 12, when the buyer corresponding to each node in the vertical row corresponds to each attribute information in the horizontal column, “1” is indicated. When the buyer does not correspond to the attribute information, “0” is indicated. Is memorized. For example, the buyer corresponding to the node N11 has the attribute information “male” “0”, the attribute information “female” “1”, the attribute information “20's” “1”, and the attribute information “ “30s” is “0”. That is, the buyer corresponding to the node N11 indicates that it is a female user in their 20s.

  The score information storage unit 125A and the second graph information storage unit 126A store the score and the second graph calculated based on the attribute information stored in the attribute information storage unit 124A. Omitted.

The second generation unit 133 according to the modified example uses the i-dimensional attribute information of the node u in the information shown in the attribute information storage unit 124A in the first index u _i and the second index v _i of the above formula (2). The score may be calculated using the value stored in the area.

For example, a node u is the node N11, when i is 1, u ₁ shows one-dimensional element of the attribute information of the node N11, a value corresponding to, for example, attribute information "male". For example, when the node u is the node N11 and i is 1, u ₁ is the one-dimensional element of the attribute information of the node u in the information shown in the attribute information storage unit 124A, for example, the attribute information “male”. The corresponding value is “0”. Further, for example, a node u is the node N11, when i is 2, u _2, during the information indicating the attribute information storage unit 124A, 2-dimensional element in the attribute information of the node u, for example, attribute information "female "1" corresponding to "". The second generation unit 133 according to the modification may calculate a score using the value stored in the attribute information storage unit 124A as described above, and generate a second graph.

  Note that the generation device 100A may store category information corresponding to the attribute information. For example, the generation device 100A may include a category information storage unit that stores category information indicating a hierarchical structure of occupations when the attribute information includes a user occupation or the like. In the above-described example, the generation apparatus 100A shows an example in which the storage unit 120A does not include the category information storage unit 123 and the attribute information storage unit 124. However, the generation apparatus 100A includes the category information storage unit 123 in the storage unit 120A. Or an attribute information storage unit 124. In addition, the generation device 100A may include a score information storage unit 125 and a second graph information storage unit 126 corresponding to the category information storage unit 123 and the attribute information storage unit 124. That is, the generation device 100 according to the embodiment and the generation device 100A according to the modification may be integrated and used as one generation device.

[6. (Category change)
Note that the generation device 100 and the generation device 100A may change the category according to the result of clustering the nodes included in the second graph, for example. This point will be described with reference to FIGS. For example, the following category change process may be performed by the generation device 100 or the second generation unit 133 of the generation device 100A. Hereinafter, a case where the generation apparatus 100 performs processing will be described as an example.

[6-1. Category integration
First, category integration will be described with reference to FIG. FIG. 13 is a diagram illustrating an example of category integration. For example, when there are a plurality of attribute information common to nodes belonging to a predetermined cluster included in the second graph, the second generation unit 133 of the generation apparatus 100 integrates categories corresponding to the attribute information.

  In the example illustrated in FIG. 13, the generation device 100 uses category information as illustrated in the category information CLT 11-1. The “first hierarchy” in the category information CLT 11-1 includes the category C3 of “first classification C” and the like. Further, the “second hierarchy” that is lower than the category C3 of “first classification C” that is the first hierarchy includes the category C31 of “second classification_1” and the like. In addition, in the “third hierarchy” below the category C31 of “second classification_1” that is the second hierarchy, the category C311 of “third classification_1”, the category C312 of “third classification_2”, and “third The category C313 of "Classification_3", the category C314 of "Third classification_4", and the like are included.

  In the example illustrated in FIG. 13, the generation device 100 clusters each node based on the connection between the nodes in the generated second graph. For example, as illustrated in the cluster list LT11, the generation apparatus 100 clusters the nodes N111, N112, and N113 connected by edges in the generated second graph as the cluster CL11. Further, for example, the generating apparatus 100 clusters other node groups connected by edges in the generated second graph as the cluster CL12.

  In the example shown in FIG. 13, as shown in the cluster list LT11, the node N111, the node N112, and the node N113 belonging to the cluster CL11 are common in that the values of the category C311 and the category C313 are large. Therefore, the generation device 100 integrates the category C311 and the category C313 into one category (step S21). For example, as illustrated in the category information CLT11-2 in FIG. 13, the generation apparatus 100 integrates the category C311 and the category C313 into the new category “Category 3_I-3” category C311-3.

  Thereby, the production | generation apparatus 100 can produce | generate the 2nd graph reflecting the attribute information of a node by using the category information which changed the category based on the result of clustering. Therefore, the generation apparatus 100 can perform more appropriate clustering. Note that, in the example of FIG. 13, in order to simplify the description, an example in which the lower hierarchy of the category C31 that is the second classification_1 that is the same second hierarchy is shown. Categories having different upper layers may be integrated based on the similarity of the attribute information of the clustered nodes. Moreover, said integration is an example and the production | generation apparatus 100 may integrate a category based on various conditions. For example, when more than half (50%) of buyers in a cluster purchase the same two categories of products, the generation apparatus 100 may integrate the two categories.

[6-2. Category division
Next, category division will be described with reference to FIG. FIG. 14 is a diagram illustrating an example of category division. For example, when there is attribute information common to nodes belonging to each of a plurality of clusters included in the second graph, the second generation unit 133 of the generation device 100 divides a category corresponding to the attribute information.

  In the example illustrated in FIG. 14, the generation apparatus 100 uses category information as illustrated in the category information CLT 21-1. The “first hierarchy” in the category information CLT 21-1 includes the category C4 of “first classification C” and the like. Further, the “second hierarchy” that is lower than the category C4 of “first classification C” that is the first hierarchy includes the category C41 of “second classification_1” and the like. In addition, the “third hierarchy”, which is lower than the “second classification_1” category C41, which is the second hierarchy, includes the “third classification_1” category C411, the “third classification_2” category C412, and the “third classification”. “Category_3” category C413 and the like are included.

  In the example illustrated in FIG. 14, the generation device 100 clusters each node based on the connection between the nodes in the generated second graph. For example, as illustrated in the cluster list LT21, the generating apparatus 100 clusters the nodes N211, N212, and N213 connected by edges in the generated second graph as the cluster CL21. Further, for example, as illustrated in the cluster list LT21, the generation apparatus 100 clusters the nodes N221 and N222 connected by edges in the generated second graph as the cluster CL22.

  In the example shown in FIG. 14, as shown in the cluster list LT21, the nodes N211 to N213 belonging to the cluster CL21 and the nodes N221 to N222 belonging to the cluster CL22 are common in that the value of the category C411 is large. Therefore, the generation device 100 divides the category C411 into two categories (step S26). For example, as illustrated in the category information CLT 21-2 in FIG. 14, the generation device 100 includes a category C411-1 of “third classification_I-1” and a category C411-2 of “third classification_I-2”. The category C411 is divided into two new categories. For example, the generation apparatus 100 includes the attribute information of the nodes N211 to N213 belonging to the cluster CL21 in the category C411-1 of “third classification_I-1”, and the category C411-2 of “third classification_I-2”. The category C411 is divided so that the attribute information of the nodes N221 to N222 belonging to the cluster CL22 is included.

  Thereby, the production | generation apparatus 100 can produce | generate the 2nd graph reflecting the attribute information of a node by using the category information which changed the category based on the result of clustering. Therefore, the generation apparatus 100 can perform more appropriate clustering. In the example of FIG. 14, for the sake of simplicity, the example in which the divided categories are arranged in the lower hierarchy of the category C41 that is the second classification_1 that is the same second hierarchy is shown. The divided categories may be arranged as a category in a lower hierarchy of an upper hierarchy different from the upper hierarchy before the division.

[7. ensemble〕
In the above-described example, the generation apparatus 100 and the generation apparatus 100A have shown the case where the second graph is generated based on one attribute information. However, the generation apparatus 100 and the generation apparatus 100A, for example, have attribute information of a plurality of categories. The second graph may be generated based on the above. In other words, when a plurality of attribute information (vectors) exist in a node, the generation device 100 and the generation device 100A do not combine them and handle them as one vector, but perform clustering corresponding to each attribute information. They may be integrated to generate a single result (second graph or cluster). For example, the process of generating the second graph based on the attribute information of a plurality of categories may be performed by the generation device 100 or the second generation unit 133 of the generation device 100A. This point will be described with reference to FIG. FIG. 15 is a diagram illustrating an example of generation of a second graph obtained by integrating a plurality of second graphs. Hereinafter, a case where the generation apparatus 100 performs processing will be described as an example. In the example of FIG. 15, the generation apparatus 100 uses, for example, category information of a product purchased by a buyer (user) corresponding to each node as the attribute information A of the first category. In FIG. 15, the generation apparatus 100 uses, for example, information regarding the demographic attribute of the buyer (user) corresponding to each node as the attribute information B of the second category.

  In the example illustrated in FIG. 15, the generation device 100 uses the first graph information as illustrated in the first graph information NL31. For example, in the example illustrated in FIG. 15, “0” is stored in an area where the row of the node ID “N311” and the column of the node ID “N312” intersect. That is, no edge is connected between the node corresponding to the buyer identified by the node ID “N311” and the node corresponding to the buyer identified by the node ID “N312”. Further, in the example illustrated in FIG. 15, an edge is connected between a node corresponding to the buyer identified by the node ID “N311” and a node corresponding to the buyer identified by the node ID “N313”. .

  In FIG. 15, the generation device 100 generates a second graph using the first graph information NL31 and the attribute information A (step S31). In the example illustrated in FIG. 15, the generation device 100 clusters each node based on the connection between the nodes in the second graph of the generated attribute information A. For example, as illustrated in the cluster list LT311, the generation apparatus 100 clusters the nodes N311, N312 and N313 connected by edges in the generated second graph as the cluster CL311.

  In FIG. 15, the generation device 100 generates the second graph using the first graph information NL31 and the attribute information B (Step S32). In the example illustrated in FIG. 15, the generation device 100 clusters each node based on the connection between the nodes in the second graph of the generated attribute information B. For example, as illustrated in the cluster list LT312, the generation apparatus 100 clusters the nodes N311 and N312 connected by edges in the generated second graph as a cluster CL321. Further, for example, as illustrated in the cluster list LT312, the generation apparatus 100 clusters the nodes N314 and N315 connected by edges in the generated second graph as the cluster CL322.

  Then, the generating apparatus 100 generates a second graph (hereinafter also referred to as “integrated second graph”) based on the second graph of the first category and the second graph of the second category (step S33). In FIG. 15, the generation device 100 generates an integrated second graph based on the second graph of attribute information A and the second graph of attribute information B. Specifically, the generating apparatus 100 generates an integrated second graph including clusters as shown in the cluster list LT31 based on the clusters as shown in the cluster list LT311 and the clusters as shown in the cluster list LT312. .

  In the example of FIG. 15, the generation apparatus 100 generates an integrated second graph by a process for obtaining a product set of a cluster as shown in the cluster list LT311 and a cluster as shown in the cluster list LT312. For example, as illustrated in the cluster list LT31, the generation apparatus 100 generates an integrated second graph in which the nodes N311 and N312 are clustered as one cluster CL31. That is, the generation device 100 generates an integrated second graph in which the node N311 and the node N312 are connected by an edge.

  Note that the generation apparatus 100 may generate the integrated second graph by a process of obtaining a union of clusters as shown in the cluster list LT311 and clusters as shown in the cluster list LT312. In this case, the generation apparatus 100 generates an integrated second graph in which the nodes N311, N312 and N313 are clustered as one cluster, and the nodes N314 and N315 are clustered as one cluster. That is, the generating apparatus 100 generates an integrated second graph in which the node N311, the node N312 and the node N313 are connected by an edge, and the node N314 and the node N315 are connected by an edge.

  As described above, the generation apparatus 100 can generate the second graph reflecting the plurality of attribute information of each node by generating the integrated second graph using the attribute information of the plurality of categories. Therefore, the generation apparatus 100 can perform more appropriate clustering. When performing the process of integrating the plurality of second graphs described above, the generation device 100 and the generation device 100A include a category information storage unit corresponding to each category, an attribute information storage unit and a score information storage unit corresponding to each attribute information Or a second graph information storage unit. The generation device 100 and the generation device 100A may include an integrated second graph information storage unit that stores the integrated second graph.

  The generation of the integrated second graph described above is an example, and the generation apparatus 100 may generate the integrated second graph by an appropriate method according to various purposes. For example, the generation apparatus 100 sets 0 and 1 bits indicating whether or not the cluster belongs to the cluster in the second graph for each attribute information, and adds the clustering results in the second graph for all the attribute information to add the heat map. You may create it. In this case, the generation apparatus 100 may extract an integrated cluster obtained by integrating the clusters of the second graph corresponding to each attribute information using a predetermined threshold value. Further, the generating apparatus 100 obtains the center of gravity of the cluster based on the result of clustering in the second graph for each attribute information, and assigns a higher score to the node as it is closer to the center of gravity, thereby creating and integrating the heat map. Clusters may be extracted. Thus, for example, the generation apparatus 100 may extract an integrated cluster using the result of clustering in the second graph of each attribute information. For example, when the cluster in the second graph of certain attribute information overlaps with a cluster in the second graph of other attribute information and a node having a predetermined threshold value or more, the generation apparatus 100 sets the overlapping node group as It may be extracted as an integrated cluster. The generation apparatus 100 may generate the integrated second graph by the process of extracting the integrated cluster. Note that the above is an example, and the generation apparatus 100 may generate the integrated second graph based on any criterion.

[8. effect〕
As described above, the generation device 100 according to the embodiment and the generation device 100A according to the modification include an acquisition unit 131 and a generation unit (in the embodiment, “second generation unit 133”; the same applies hereinafter). Have. The acquisition unit 131 acquires a first graph including a plurality of nodes corresponding to each of the subjects on the network and an edge connecting nodes having a predetermined correspondence relationship. The generation unit is configured to determine whether the first node and the second node are based on the relationship between the first node, the second node, and the third node among the plurality of nodes in the first graph acquired by the acquisition unit 131. A second graph in which the edges of the second line are changed is generated.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modification may be configured such that the relationship between the first node and the second node is based on the relationship between the first node, the second node, and the third node. By generating the second graph in which the edge is changed, it is possible to appropriately generate a graph that connects the subjects on the network based on a desired relationship.

  Further, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the generation unit is based on the relationship with the third node in which the first node and the second node are connected by an edge. A second graph is generated.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modification may be configured on the network based on the relationship between the first node and the second node connected to each other by an edge. It is possible to appropriately generate a graph that connects the subjects based on a desired relationship.

  Further, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the generation unit is based on the similarity between the first node and the third node and the similarity between the second node and the third node. The second graph is generated.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modification have the network based on the similarity between the first node and the third node and the similarity between the second node and the third node. It is possible to appropriately generate a graph that connects the above subjects based on a desired relationship.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the generation unit performs the second operation based on the number of third nodes in which the first node and the second node are connected by an edge. Generate a graph.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modified example determine the subject on the network based on the number of third nodes in which the first node and the second node are connected by an edge. A graph to be connected based on a desired relationship can be appropriately generated.

  Further, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 sets the second main body having a predetermined relationship with each first main body as the first main body that satisfies the predetermined condition. A first graph in which corresponding nodes are connected by an edge is acquired.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modified example are configured so that the second main body having a predetermined relationship with each first main body corresponds to the first main body that satisfies the predetermined condition. Can be appropriately generated from the first graph in which the entities on the network are connected based on the desired relationship.

  Further, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 has a predetermined condition that a set of sellers as second entities having a predetermined relationship with a buyer as a first entity is a predetermined condition. A first graph in which nodes corresponding to buyers satisfying the condition are connected by an edge is acquired.

  Accordingly, the generation device 100 according to the embodiment and the generation device 100A according to the modification include a node corresponding to a buyer that satisfies a predetermined condition by a set of sellers having a predetermined relationship with each buyer among nodes corresponding to the buyer. It is possible to appropriately generate a graph that connects the subjects on the network based on a desired relationship from the first graph in which the edges are connected by edges.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 has a predetermined degree of similarity between a set of sellers who have made a transaction a predetermined number of times or more among nodes corresponding to the buyer. A first graph in which nodes that are equal to or greater than the threshold value are connected by edges is acquired.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modification have a similarity between a set of sellers who have made a transaction a predetermined number of times or more among nodes corresponding to the buyer with a predetermined threshold value or more. It is possible to appropriately generate a graph that connects the subjects on the network based on a desired relationship from the first graph in which the nodes are connected by edges.

  Further, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 acquires a first graph in which attribute information is associated with each node. In addition, the generation unit generates the second graph based on the attribute information of the first node and the relationship between the attribute information of the second node and the attribute information of the third node.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modified example are connected to the network based on the attribute information of the first node and the relationship between the attribute information of the second node and the attribute information of the third node. It is possible to appropriately generate a graph that connects the subjects in, based on a desired relationship.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 associates category information of a product having a predetermined relationship with a user corresponding to each node as attribute information. Obtain the first graph.

  Accordingly, the generation apparatus 100 according to the embodiment and the generation apparatus 100A according to the modification include the first graph in which the category information of the product having a predetermined relationship with the user corresponding to each node is associated with each node as attribute information. Therefore, it is possible to appropriately generate a graph that connects the subjects on the network based on a desired relationship.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 includes, as attribute information, first information in which category information of a product purchased by a user corresponding to each node is associated with each node. Get the graph.

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modification can generate a network from the first graph in which the category information of the product purchased by the user corresponding to each node is associated with each node as attribute information. It is possible to appropriately generate a graph that connects the above subjects based on a desired relationship.

  Further, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 acquires, as attribute information, a first graph in which user attribute information corresponding to each node is associated with each node. .

  As a result, the generation device 100 according to the embodiment and the generation device 100A according to the modified example identify the subject on the network from the first graph in which user attribute information corresponding to each node is associated with each node as attribute information. A graph to be connected based on a desired relationship can be appropriately generated.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the generation unit changes the category corresponding to the attribute information based on the attribute information of the nodes belonging to the predetermined cluster included in the second graph. To do.

  Accordingly, the generation device 100 according to the embodiment and the generation device 100A according to the modification may change the category corresponding to the attribute information based on the attribute information of the nodes belonging to the predetermined cluster included in the second graph. The graph that connects the subjects on the network based on a desired relationship can be appropriately generated.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the generation unit corresponds to the attribute information when there is a plurality of attribute information common to nodes belonging to the predetermined cluster included in the second graph. Integrate categories.

  Thereby, the generating apparatus 100 according to the embodiment and the generating apparatus 100A according to the modification generate the second graph reflecting the attribute information of the node by using the category information in which the category is changed based on the clustering result. be able to. Therefore, the generation apparatuses 100 and 100A can perform more appropriate clustering. Accordingly, the generation devices 100 and 100A can appropriately generate a graph that connects the subjects on the network based on a desired relationship.

  In the generation device 100 according to the embodiment and the generation device 100A according to the modification, the generation unit includes attribute information common to nodes belonging to each of a plurality of clusters included in the second graph. Split the corresponding category.

  Thereby, the generating apparatus 100 according to the embodiment and the generating apparatus 100A according to the modification generate the second graph reflecting the attribute information of the node by using the category information in which the category is changed based on the clustering result. be able to. Therefore, the generation apparatuses 100 and 100A can perform more appropriate clustering. Accordingly, the generation devices 100 and 100A can appropriately generate a graph that connects the subjects on the network based on a desired relationship.

  In addition, in the generation device 100 according to the embodiment and the generation device 100A according to the modification, the acquisition unit 131 displays a first graph in which the attribute information of the first category and the attribute information of the second category are associated with each node. get. In addition, the generation unit may determine the first category attribute information based on the relationship between the first category attribute information of the first node, the first category attribute information of the second node, and the first category attribute information of the third node. A second graph is generated, and based on the relationship between the attribute information of the second category of the first node and the attribute information of the second category of the second node and the attribute information of the second category of the third node, the second category The second graph is generated, and the second graph is generated based on the second graph of the first category and the second graph of the second category.

  Accordingly, the generation device 100 according to the embodiment and the generation device 100A according to the modification generate the integrated second graph using the attribute information of the plurality of categories, thereby reflecting the plurality of attribute information of each node. Two graphs can be generated. Therefore, the generation apparatuses 100 and 100A can perform more appropriate clustering. That is, the generation devices 100 and 100A can appropriately generate a graph that connects the subjects on the network based on a desired relationship.

[9. Hardware configuration)
The generation apparatus 100 according to the embodiment and the generation apparatus 100A according to the modification described above are realized by a computer 1000 having a configuration as illustrated in FIG. 16, for example. FIG. 16 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the generation device. The computer 1000 includes a CPU 1100, RAM 1200, ROM 1300, HDD 1400, communication interface (I / F) 1500, input / output interface (I / F) 1600, and media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via a predetermined network N and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the generation apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 implements the function of the control unit 130 by executing a program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them. However, as another example, these programs may be acquired from other devices via the predetermined network N.

  As described above, some of the embodiments of the present application have been described in detail with reference to the drawings. However, these are merely examples, and various modifications based on the knowledge of those skilled in the art including the aspects described in the disclosure line of the invention, It is possible to implement the present invention in other forms with improvements.

[10. Others]
In addition, among the processes described in the above embodiments, all or a part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed All or a part of the above can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured. For example, the device that generates the first graph and the device that generates the second graph may be separate.

  Moreover, each embodiment mentioned above can be combined suitably in the range which does not contradict a process content.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

100 Generating Device 121 Transaction Information Storage Unit 122 First Graph Information Storage Unit 123 Category Information Storage Unit 124 Attribute Information Storage Unit 125 Score Information Storage Unit 126 Second Graph Information Storage Unit 130 Control Unit 131 Acquisition Unit 132 First Generation Unit 133 First 2 generator 134 transmitter

Claims (17)

An acquisition unit that acquires a first graph including a plurality of nodes corresponding to each of the subjects on the network and an edge connecting nodes having a predetermined correspondence;
Based on the relationship between the first node, the second node, and the third node among the plurality of nodes in the first graph acquired by the acquisition unit, between the first node and the second node A generating unit for generating a second graph in which the edge of
A generating apparatus comprising: The generator is
The generating apparatus according to claim 1, wherein the second graph is generated based on a relationship with a third node in which the first node and the second node are connected by an edge. The generator is
The second graph is generated based on a similarity between the first node and the third node and a similarity between the second node and the third node. Generator. The generator is
The generating apparatus according to claim 3, wherein the second graph is generated based on the number of the third nodes in which the first node and the second node are connected by an edge. The acquisition unit
The second graph having a predetermined relationship with each first entity acquires the first graph in which nodes corresponding to the first entity satisfying a predetermined condition are connected by an edge. The production | generation apparatus of any one of -4. The acquisition unit
Obtaining the first graph in which a set of sellers as second entities having a predetermined relationship with a buyer as the first entity connects nodes corresponding to buyers satisfying a predetermined condition with edges. The generating apparatus according to claim 5, wherein the generating apparatus is characterized in that: The acquisition unit
The first graph in which nodes having a similarity equal to or higher than a predetermined threshold among nodes corresponding to a buyer among the nodes corresponding to the buyer is connected by an edge is acquired. The generation device according to claim 6. The acquisition unit
Obtaining the first graph in which attribute information is associated with each node;
The generator is
The said 2nd graph is produced | generated based on the relationship between the attribute information of the said 1st node, the attribute information of the said 2nd node, and the attribute information of the said 3rd node, The any one of Claims 1-7 characterized by the above-mentioned. The generating device according to claim 1. The acquisition unit
9. The generation apparatus according to claim 8, wherein category information of a product having a predetermined relationship with a user corresponding to each node is acquired as attribute information in the first graph in which each node is associated. The acquisition unit
The generation apparatus according to claim 9, wherein the first graph in which category information of a product purchased by a user corresponding to each node is associated with each node is acquired as attribute information. The acquisition unit
The generation apparatus according to claim 8, wherein the first graph in which attribute information of a user corresponding to each node is associated with each node is acquired as attribute information. The generator is
The generation device according to any one of claims 8 to 11, wherein a category corresponding to attribute information is changed based on attribute information of nodes belonging to a predetermined cluster included in the second graph. The generator is
The generation apparatus according to claim 12, wherein when there are a plurality of attribute information common to nodes belonging to a predetermined cluster included in the second graph, the categories corresponding to the attribute information are integrated. The generator is
14. The generation according to claim 12, wherein when there is attribute information common to nodes belonging to each of the plurality of clusters included in the second graph, a category corresponding to the attribute information is divided. apparatus. The acquisition unit
Obtaining a first graph in which attribute information of the first category and attribute information of the second category are associated with each node;
The generator is
Based on the relationship between the attribute information of the first category of the first node and the attribute information of the first category of the second node and the attribute information of the first category of the third node, a second graph of the first category is generated. Generating a second graph of the second category based on the attribute information of the second category of the first node and the relationship between the attribute information of the second category of the second node and the attribute information of the second category of the third node The generation device according to claim 8, wherein the second graph is generated based on the second graph of the first category and the second graph of the second category. . A generation method executed by a computer,
An acquisition step of acquiring a first graph including a plurality of nodes corresponding to each of the subjects on the network and an edge connecting nodes having a predetermined correspondence relationship;
Based on the relationship between the first node, the second node, and the third node among the plurality of nodes in the first graph obtained by the obtaining step, the relationship between the first node and the second node. A generating step of generating a second graph in which the edge of
A generation method comprising: An acquisition procedure for acquiring a first graph including a plurality of nodes corresponding to each of the subjects on the network and an edge connecting nodes having a predetermined correspondence;
Among the plurality of nodes in the first graph acquired by the acquisition procedure, based on the relationship between the first node, the second node, and the third node, between the first node and the second node. A generation procedure for generating a second graph in which the edge of
A program for causing a computer to execute.

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