CN112132305A - Node type determination method, related device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a node type determination method, a related device, equipment and a storage medium, wherein the method comprises the following steps: the terminal equipment determines at least one neighbor node of a target node; calculating at least one first probability that any neighbor node of the target node transmits the target label to the target node based on various types of edges connected with the target node, so as to obtain a first probability set that each neighbor node transmits the target label to the target node based on various types of edges; determining a first probability vector of the target node based on the same type of edge receiving target labels based on the first probability set to obtain a first probability vector set of the target node based on multiple types of edge receiving target labels; obtaining a second probability of the target node receiving the target label based on the first probability vector set; and determining the node category of the target node based on the second probability. By adopting the embodiment of the invention, the category of any node in any social network can be predicted, and the applicability is high.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a node class determination method, a related apparatus, a device, and a storage medium.
Background
At present, social communication (social influence communication) has a wide application scene, and the social influence communication is mainly realized based on label communication in a social network, but most of traditional label communication methods are based on homogeneous networks. Since nodes in a homogeneous network belong to the same type of object (entity) and are comparable between the nodes, the class of each node can be predicted based on the propagation of labels in the homogeneous network.
However, for the heterogeneous network, different nodes and edges between the nodes are of various types, each type of node has different characteristics, and the existing label propagation method cannot effectively judge whether each node in the heterogeneous network is affected by other nodes, and is relatively limited.
Disclosure of Invention
The embodiment of the invention provides a node type determination method, a related device, equipment and a storage medium, which can predict the type of any node in any social network and have high applicability.
In a first aspect, an embodiment of the present invention provides a method for determining a node category, where the method includes:
the method comprises the steps that terminal equipment determines at least one neighbor node of a target node, wherein the neighbor node is a node which is connected with the target node based on at least one type of edge;
the terminal equipment calculates at least one first probability that any neighbor node of the target node propagates a target label to the target node based on various types of edges connected with the target node, wherein one type of edge corresponds to one first probability, so as to obtain a first probability set that each neighbor node propagates the target label to the target node based on various types of edges;
the terminal device determines, based on the first probability set, first probability vectors of the target nodes receiving the target labels based on the same type of edges, so as to obtain a first probability vector set of the target nodes receiving the target labels based on multiple types of edges;
the terminal equipment obtains a second probability of receiving the target label by the target node based on the first probability vector set;
and the terminal equipment determines the node type of the target node based on the second probability, and outputs the node type of the target node to a user interaction interface of the terminal equipment so as to be displayed for a user.
With reference to the first aspect, in a possible implementation manner, the calculating, by the terminal device, at least one first probability that any neighbor node of the target node propagates a target label to the target node based on various types of edges connected to the target node includes:
the terminal equipment acquires at least one edge attribute of any type of edge connected with any neighbor node and the target node;
the terminal equipment determines the attribute weight of each edge attribute in the at least one edge attribute;
the terminal device determines an edge weight of the edge of any type based on the attribute weight of each edge attribute, and determines the edge weight as a first probability that any neighbor node propagates a target label to the target node based on the edge of any type, so as to obtain first probabilities that any neighbor node propagates the target label to the target node based on each type of edge between the neighbor node and the target node.
With reference to the first aspect, in a possible implementation manner, the determining, by the terminal device, a first probability vector that the target node receives the target label based on an edge of the same type based on the first probability set includes:
the terminal equipment determines at least one first probability corresponding to the edges with the edge types belonging to the same type from the first probability set;
the terminal device constructs a first probability vector based on the at least one first probability, wherein the target node receives the target label based on the same type of edge.
With reference to the first aspect, in a possible implementation manner, the determining, by the terminal device, multiple neighbor nodes of a target node includes:
the terminal device obtains an association relation between nodes in at least one social network, determines at least one node having an association relation with a target node from the nodes based on the association relation between the nodes, and determines the at least one node as a neighbor node of the target node.
With reference to the first aspect, in a possible implementation manner, the obtaining, by the terminal device, a second probability of the target node receiving the target label based on the first set of probability vectors includes:
the terminal equipment determines each type weight corresponding to each type in the types of the edges, wherein one type corresponds to one type weight;
the terminal device determines a second probability that the target node receives the target label based on the type weights and the first set of probability vectors.
With reference to the first aspect, in a possible implementation manner, the determining, by the terminal device, a node type of the target node based on the second probability includes:
the terminal equipment compares the second probability with a preset threshold value;
and when the second probability is greater than or equal to the preset threshold, the terminal device determines that the node type of the target node is the node type marked by the target label.
With reference to the first aspect, in a possible implementation manner, the neighbor nodes of the target node include a first-degree neighbor node of the target node and a second-degree neighbor node of the target node, where the first-degree neighbor node is a node connected to the target node based on at least one type of edge, and the second-degree neighbor node is any node connected to the target node based on any one-degree neighbor node;
the method further comprises the following steps:
the terminal device determines that the target node receives second probability vectors of the target labels transmitted from all second-degree neighbor nodes based on the same type of edges so as to obtain a second probability vector set that the target node transmits the target labels from all second-degree neighbor nodes based on multiple types of edges;
and the terminal equipment obtains a second probability of receiving the target label by the target node based on the first probability vector set and the second probability vector set.
In a second aspect, an embodiment of the present invention provides a node type determining apparatus, where the apparatus includes:
a node determining module, configured to determine at least one neighbor node of a target node, where the neighbor node is a node connected to the target node based on at least one type of edge;
a first probability determination module, configured to calculate at least one first probability that any neighboring node of the target node propagates a target label to the target node based on various types of edges connected to the target node, where one type of edge corresponds to one first probability, so as to obtain a first probability set that each neighboring node propagates the target label to the target node based on various types of edges;
a probability vector determining module, configured to determine, based on the first probability set, a first probability vector that the target node receives the target tag based on a same type of edge, so as to obtain a first probability vector set that the target node receives the target tag based on multiple types of edges;
a second probability determination module, configured to obtain, based on the first set of probability vectors, a second probability that the target node receives the target label;
and the node type determining module is used for determining the node type of the target node based on the second probability and outputting the node type of the target node to a user interaction interface of the terminal equipment so as to be displayed for a user.
With reference to the second aspect, in a possible implementation manner, the first probability determining module includes:
an edge attribute obtaining unit, configured to obtain at least one edge attribute of an edge of any type, where any neighbor node is connected to the target node;
an attribute weight determining unit for determining an attribute weight of each edge attribute of the at least one edge attribute;
and an edge weight determining unit, configured to determine an edge weight of the edge of any type based on the attribute weight of each edge attribute, and determine the edge weight as a first probability that any neighbor node propagates a target label to the target node based on the edge of any type, so as to obtain first probabilities that any neighbor node propagates the target label to the target node based on each type of edge between the neighbor node and the target node.
With reference to the second aspect, in a possible implementation manner, the probability vector determination module is configured to:
determining at least one first probability corresponding to the edges with the edge types belonging to the same type from the first probability set;
and constructing a first probability vector based on the at least one first probability that the target node receives the target label based on the same type of edge.
With reference to the second aspect, in a possible implementation manner, the node determining module is configured to:
acquiring an association relation between nodes in at least one social network, determining at least one node having an association relation with a target node from the nodes based on the association relation between the nodes, and determining the at least one node as a neighbor node of the target node.
With reference to the second aspect, in a possible implementation manner, the second probability determining module is configured to:
determining each type weight corresponding to each type in the types of the edges, wherein one type corresponds to one type weight;
determining a second probability that the target node receives the target label based on the respective type weights and the first set of probability vectors.
With reference to the second aspect, in a possible implementation manner, the node category determining module includes:
a comparing unit, configured to compare the second probability with a preset threshold;
a category determining unit, configured to determine that the node category of the target node is the node category marked by the target label when the second probability is greater than or equal to the preset threshold.
With reference to the second aspect, in a possible implementation manner, the neighbor nodes of the target node include a first-degree neighbor node of the target node and a second-degree neighbor node of the target node, where the first-degree neighbor node is a node connected to the target node based on at least one type of edge, and the second-degree neighbor node is any node connected to the target node based on any one-degree neighbor node;
the probability vector determining module is further configured to determine that the target node receives, based on edges of the same type, second probability vectors of the target labels propagated from the respective two-degree neighbor nodes, so as to obtain a set of second probability vectors that the target node receives, based on edges of multiple types, the target labels propagated from the respective two-degree neighbor nodes;
the second probability determining module is further configured to obtain a second probability that the target node receives the target label based on the first set of probability vectors and the second set of probability vectors.
In a third aspect, an embodiment of the present invention provides a terminal device, where the terminal device includes a processor and a memory, and the processor and the memory are connected to each other. The memory is adapted to store a computer program enabling the terminal device to perform the method provided by the first aspect and/or any one of the possible implementations of the first aspect,
in a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, where the computer program is executed by a processor to implement the method provided by the first aspect and/or any one of the possible implementation manners of the first aspect.
In the embodiment of the invention, the node relations of different types in different social networks are converted into the node relations of the same type, so that the limitation of the different social networks on the node relations can be eliminated, and the node classes of any node in the different social networks can be predicted. By calculating attribute weights for the respective edge attributes of each type of edge, a respective weight of each edge attribute in its corresponding type of edge may be determined. Therefore, the edge weight of each type of edge can be accurately and reasonably determined based on the attribute weight of each edge attribute, and the applicability is high.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of an architecture of a social network provided by an embodiment of the present invention;
fig. 2 is a flowchart illustrating a node type determining method according to an embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a conversion of a node association relationship according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for calculating a first probability according to an embodiment of the present invention;
fig. 5 is a scene schematic diagram of a neighbor node according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a node category determining apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an architecture of a social network according to an embodiment of the present invention. The network architecture comprises a plurality of users, equipment and a network used for connecting the plurality of users in series, and the like. As shown in fig. 1, a plurality of users may form a social network 1 based on the terminal 100 and the terminal 200, some users may form a social network 3 based on Wifi300 and Wifi400, and another part of users may form a social network 2 based on other terminals, such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Mobile Internet Device (MID), a wearable device (e.g., a smart watch, a smart bracelet, etc.), and the like. The users in the social networks 1, 2, and 3 may propagate social information based on a certain social manner, for example, social information may be propagated based on a friend circle and an advertisement of the friend circle, and after seeing the social behavior (sending the friend circle, sharing the public number text, and praise the advertisement) of the user B, the user a may generate an interaction of the social information with the user B, such as commenting the friend circle of the user B, forwarding an article shared by the user B, and praise the advertisement praised by the user B. Briefly, each user in the social network may be considered as each node in the social network, and each node may propagate a tag to other nodes associated therewith to assign the tag to the other nodes, thereby changing the node category of the node receiving the tag propagated in the social network.
Referring to fig. 2, fig. 2 is a flowchart illustrating a node class determining method according to an embodiment of the present invention. The method for determining the node type provided by the embodiment of the invention can comprise the following steps 101-104:
101. the terminal device determines at least one neighbor node of the target node.
In some possible embodiments, the actual social network is composed of multiple devices, connections between users and/or behavioral relationships, and different users can be associated with other users through multiple devices, networks. Therefore, for any one node in the social network, only a part of other nodes in the social network have various association relations with the node. Therefore, when determining the node type of a certain node (for convenience of description, the certain node may be represented by a target node, which will not be described further below), the terminal device may obtain an association relationship between nodes in at least one social network where the target node is located, determine, based on the association relationship between the nodes, at least one node having an association relationship with the target node, and determine the at least one node as a neighbor node of the target node, so that the type of the target node may be determined based on a probability that the neighbor node of the target node propagates a label to the target node.
In some possible embodiments, since the target node may exist in different social networks and there is a difference between the types of node association relations in the different social networks, it is normally impossible to directly compare the node relations between the two different social networks. Furthermore, the types of the association relations between the nodes in different social networks are not necessarily the same type of association relation, so that before determining the neighbor node of the target node, the previous association relations between the nodes in different social networks can be converted into the same type of association relation, so as to avoid the limitation that the neighbor node cannot be determined due to the different association relations between the nodes in different social networks. Specifically, referring to fig. 3, fig. 3 is a schematic diagram illustrating a conversion of a node association relationship according to an embodiment of the present invention. As shown in fig. 2, the node B, C, D is associated with the node a through some association relationship, i.e., the node B, the node C, and the node D establish an association relationship with each other through the node a. If the node B, the node C, and the node D belong to nodes in different social networks, although the node B, the node C, and the node D have some association relationship with each other through the node a, the association relationship among the node B, the node C, and the node D cannot be directly determined. At this time, node B, node C, and node D may be combined into an incidence relation fully-connected graph to represent the incidence relation established by node B, node C, and node D based on node a. For example, when node a is a device, and node B, node C, and node D are three users respectively associated with the device, the user association relationship established by the three users based on the device may be represented based on a full connectivity graph of the user association relationship among the three users.
102. The terminal device calculates at least one first probability that any neighbor node of the target node propagates the target label to the target node based on various types of edges connected to the target node.
In some possible embodiments, after the terminal device determines at least one neighbor node of the target node, at least one first probability that any neighbor node of the target node propagates the target label to the target node based on various types of edges connected to the target node may be calculated. A first set of probabilities is thereby available for the table node to propagate the target label to the target node based on the various types of edges. Wherein, one type of edge between any neighbor node and the target node corresponds to one first probability. Specifically, the manner of calculating the first probability may be referred to in fig. 4, and fig. 4 is a flowchart of a method for calculating the first probability according to an embodiment of the present invention. The method for calculating the first probability provided by the embodiment of the invention may include the following steps 201-204:
201. the terminal equipment acquires at least one edge attribute of any type of edge connected with any neighbor node and a target node.
In some possible embodiments, the target node and any neighboring node may be connected to each other based on at least one type of edge, and in different social scenarios of different types of social networks, the type of edge between the target node and any neighboring node is also different, and may be determined based on an actual application scenario, which is not limited herein. For example, the edge of a certain type, which is connected to any node of the target node, may represent a transfer relationship between two nodes, may represent a wifi connection relationship between nodes, may also represent a chat relationship between two nodes, and the like, and is not limited herein. For any type of edge between the target node and any neighbor node, the terminal device may obtain different edge attributes in any type of edge, so that the different edge attributes in any type of edge may be based on the different edge attributes. For example, for an edge representing a transfer relationship, the edge attribute of the edge may include attributes such as transfer amount and transfer frequency, for an edge representing a wifi connection relationship between nodes, the edge attribute of the edge may include attributes such as connection duration and connection frequency, and the type and number of the edge attribute of each type of edge may be determined based on the actual type of edge, which is not limited herein.
202. The terminal device determines an attribute weight of each edge attribute in the at least one edge attribute.
In some possible embodiments, after the terminal device determines at least one edge attribute of any type of edge to which any neighboring node is connected to the target node, the terminal device may determine edge values corresponding to different edge attributes and further calculate an attribute weight of each edge attribute. Because the edge values of different edge attributes have different dimensions, after determining the edge values corresponding to different edge attributes, the terminal device can perform data normalization processing on the edge values of different dimensions to eliminate the influence caused by different dimensions of different edge values. Wherein, the normalization processing mode of the specific data can be based on
And realizing, wherein k is a normalization coefficient, and x is an edge value corresponding to each edge attribute. Based onThe edge values after normalization processing can determine the attribute weight of each edge attribute based on the normalization value of each edge value, wherein the attribute weight of each edge attribute is
203. The terminal equipment determines the edge weight of any type of edge based on the attribute weight of each edge attribute.
In some possible embodiments, after the terminal device calculates each edge attribute weight of any type of edge between the target node and any neighboring node, the terminal device may determine an edge weight of any type of edge based on the attribute weights of each edge attribute. Specifically, when there is only one edge attribute of any type of edge, the attribute weight of the edge attribute may be determined as the edge weight of any type of edge. When any of the above types of edges have two edge attributes, it may be based on W ═ W (W)1+w2)-(w1*w2) Determining an edge weight for an edge of any of the above types, wherein w1And w2Respectively, the edge weight of any type of edge, and W represents the edge weight of any type of edge. Thus, it is not difficult to derive when any of the above types of edges have n edge attributes, which can be based on
To derive edge weights for edges of any of the types described above.
204. The terminal equipment determines the edge weight as a first probability that any neighbor node transmits the target label to the target node based on any type of edge so as to obtain each first probability that any neighbor node transmits the target label to the target node based on various types of edges between the neighbor node and the target node.
In some possible embodiments, after obtaining the edge weight W of any type of edge, the terminal device may determine the edge weight W of any type of edge as a first probability that any neighbor node propagates the target label to the target node based on any type of edge. Based on the method, the terminal device can obtain a plurality of first probabilities that any one of the neighbor nodes transmits the target label to the target node based on various types of edges between the neighbor node and the target node, and further obtain a first probability set that each neighbor node transmits the target label to the target node based on various types of edges. In brief, the first probability set includes a first probability that each neighboring node propagates the target label to the target node based on each type of edge.
In the embodiment of the present invention, by calculating the attribute weight of each edge attribute of each type of edge, the corresponding proportion of each edge attribute in the edge of the corresponding type thereof can be determined. Therefore, the edge weight of each type of edge can be accurately and reasonably determined based on the attribute weight of each edge attribute, and the applicability is high.
103. The terminal equipment determines a first probability vector of the target node based on the same type of edge receiving target labels based on the first probability set so as to obtain a first probability vector set of the target node based on multiple types of edge receiving target labels.
In some possible embodiments, the terminal device may determine, from the first probability set, at least one first probability corresponding to an edge of which the edge type belongs to the same type, where the at least one first probability may include a plurality of first probabilities that different neighboring nodes rebroadcast the target label to the target node based on different edges of the same edge type. That is, the terminal device may classify each first probability in the first probability sets based on the type of the edge to obtain a plurality of sub first probability sets, where at least one first probability included in each sub first probability set is a first probability corresponding to the edge of the same type. Meanwhile, the terminal device can construct a first probability vector p of the target node receiving the target label based on the same type of edge based on each sub first probability setsWhere s represents the type of edge, e.g., p is a first probability vector for receiving the target label based on the first type of edges1Receiving a first probability vector of the target label based on the edge of the second type as ps2Thereby obtaining a first probability direction of the target node receiving the target label based on multiple types of edgesThe set of quantities P.
104. And the terminal equipment obtains a second probability of the target node receiving the target label based on the first probability vector set.
In some possible embodiments, after the terminal device determines that the target node receives the first probability vector of the target tag based on multiple types of edges, in combination with P, nodes with different importance degrees may affect the propagation probability of the tag in the social network due to different importance degrees of different nodes in the social network. Therefore, a new first set of probability vectors S is obtained based on the importance of the target label, where S ═ α P + (1- α) V, P is the first set of probability vectors obtained above, V represents the importance of the target node, and V ═ 1- α, α ≦ 0 ≦ 1. Based on the terminal device, respective type weights corresponding to various types of the edge types can be determined, and each type corresponds to a type weight. So that the terminal equipment can be based on
And determining a second probability of receiving the target label by the target node, wherein Y is the type weight corresponding to each edge type, X is the probability of receiving the target label by the target node based on the same type of edge, and m is the number of edge types.
In some possible embodiments, the neighbor nodes of the target node may include multiple-degree neighbor nodes, please refer to fig. 5, and fig. 5 is a scene diagram of the neighbor nodes provided in the embodiment of the present invention. Referring to fig. 5, when node a is a target node and the first-degree neighbor nodes (node B1, node B2, node B3, and node B4) and second-degree neighbor nodes (node C1 and node C2) of the target node are included in the neighbor nodes, the terminal device may first calculate a probability vector that the target node receives a target label propagated from each first-degree neighbor node (node B1, node B2, node B3, and node B4) based on the same type of edge, so as to obtain a probability vector set that the target node propagates the target label from each first-degree neighbor node (node B1, node B2, node B3, and node B4) based on multiple types of edge reception. The terminal device may calculate probability vectors that the target node receives the target label propagated from the respective two-degree neighbor nodes (node C1 and node C2) based on the same type of edge, thereby obtaining a set of probability vectors that the target node receives the target label propagated from the respective two-degree neighbor nodes (node C1 and node C2) based on the plurality of types of edges. The probability vector propagated by the target node a based on the multi-type edge receiving two-degree neighbor node C2 may be determined by the one-degree neighbor ground B3 based on the propagation probability of the multi-type edge with the node B2 and the propagation probability of the one-degree neighbor node propagating the target label to the target node a based on the multi-type edge, which is not limited herein. And obtaining a second probability vector set of the target node receiving the target label propagated by each neighbor node based on the multiple types of edges based on the two probability vector sets, thereby obtaining a second probability of the target node receiving the target label based on the second probability vector set. The specific calculation manner may be based on the above-described implementation manner, and is not limited herein.
105. And the terminal equipment determines the node type of the target node based on the second probability, and outputs the node type of the target node to a user interaction interface of the terminal equipment so as to be displayed for a user.
In some possible embodiments, after the terminal device determines the second probability that the target node receives the target tag, the second probability may be compared with a preset threshold. When the second probability is greater than or equal to the preset threshold, the terminal device may determine that the node type of the target node is the node type marked by the target label. Optionally, when the second probability is smaller than the preset threshold, the target label propagated to the target node may be determined to be a negative-type label (e.g., gambling, fraud, etc.), and after the terminal device determines the node type of the target node, the node type of the target node may be output to the user interaction interface of the terminal device for presentation to the user.
In the embodiment of the invention, the node relations of different types in different social networks are converted into the node relations of the same type, so that the limitation of the different social networks on the node relations can be eliminated, and the node classes of any node in the different social networks can be predicted. By calculating attribute weights for the respective edge attributes of each type of edge, a respective weight of each edge attribute in its corresponding type of edge may be determined. Therefore, the edge weight of each type of edge can be accurately and reasonably determined based on the attribute weight of each edge attribute, and the applicability is high.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a node class determination apparatus according to an embodiment of the present invention. The node type determination device provided by the embodiment of the invention comprises:
a node determining module 31, configured to determine at least one neighbor node of a target node, where the neighbor node is a node connected to the target node based on at least one type of edge;
a first probability determination module 32, configured to calculate at least one first probability that any neighboring node of the target node propagates the target label to the target node based on various types of edges connected to the target node, where one type of edge corresponds to one first probability, so as to obtain a first probability set that each neighboring node propagates the target label to the target node based on various types of edges;
a probability vector determining module 33, configured to determine, based on the first probability set, a first probability vector that the target node receives the target tag based on a same type of edge, so as to obtain a first probability vector set that the target node receives the target tag based on multiple types of edges;
a second probability determining module 34, configured to obtain a second probability that the target node receives the target label based on the first set of probability vectors;
and a node type determining module 35, configured to determine a node type of the target node based on the second probability, and output the node type of the target node to a user interaction interface of the terminal device for presentation to a user.
In some possible embodiments, the first probability determination module 32 includes:
an edge attribute obtaining unit 321, configured to obtain at least one edge attribute of an edge of any type, where any neighboring node is connected to the target node;
an attribute weight determining unit 322, configured to determine an attribute weight of each edge attribute in the at least one edge attribute;
an edge weight determining unit 323, configured to determine an edge weight of the edge of any type based on the attribute weight of the edge attribute, and determine the edge weight as a first probability that the neighbor node propagates the target label to the target node based on the edge of any type, so as to obtain first probabilities that the neighbor node propagates the target label to the target node based on the edge of each type between the neighbor node and the target node.
In some possible embodiments, the probability vector determination module 33 is configured to:
determining at least one first probability corresponding to the edges with the edge types belonging to the same type from the first probability set;
and constructing a first probability vector based on the at least one first probability that the target node receives the target label based on the same type of edge.
In some possible embodiments, the node determining module 31 is configured to:
acquiring an association relation between nodes in at least one social network, determining at least one node having an association relation with a target node from the nodes based on the association relation between the nodes, and determining the at least one node as a neighbor node of the target node.
In some possible embodiments, the second probability determination module 34 is configured to:
determining each type weight corresponding to each type in the types of the edges, wherein one type corresponds to one type weight;
determining a second probability that the target node receives the target label based on the respective type weights and the first set of probability vectors.
In some possible embodiments, the node category determining module 35 includes:
a comparing unit 351, configured to compare the second probability with a preset threshold;
a category determining unit 352, configured to determine that the node category of the target node is the node category marked by the target label when the second probability is greater than or equal to the preset threshold.
In some possible embodiments, the neighbor nodes of the target node include a first-degree neighbor node of the target node and a second-degree neighbor node of the target node, where the first-degree neighbor node is a node connected to the target node based on at least one type of edge, and the second-degree neighbor node is any node connected to the target node based on any one-degree neighbor node;
the probability vector determining module 33 is further configured to determine that the target node receives, based on the same type of edge, second probability vectors of the target labels propagated from the two-degree neighbor nodes, so as to obtain a set of second probability vectors that the target node receives, based on multiple types of edges, the target labels propagated from the two-degree neighbor nodes;
the second probability determining module 34 is further configured to obtain a second probability that the target node receives the target label based on the first set of probability vectors and the second set of probability vectors.
In a specific implementation, the node type determining apparatus may execute, through each built-in module and/or unit thereof, the implementation manner provided in each step in fig. 1 to fig. 5, which is not described herein again.
In the embodiment of the invention, the node relations of different types in different social networks are converted into the node relations of the same type, so that the limitation of the different social networks on the node relations can be eliminated, and the node classes of any node in the different social networks can be predicted. By calculating attribute weights for the respective edge attributes of each type of edge, a respective weight of each edge attribute in its corresponding type of edge may be determined. Therefore, the edge weight of each type of edge can be accurately and reasonably determined based on the attribute weight of each edge attribute, and the applicability is high.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present invention. The electronic device 1000 may include: the processor 1001, the network interface 1004, and the memory 1005, the electronic device 1000 may further include: a user interface 1003, and at least one communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display) and a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a standard wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in fig. 7, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a device control application program.
In the electronic device 1000 shown in fig. 7, the network interface 1004 may provide a network communication function; the user interface 1003 is an interface for providing a user with input; and the processor 1001 may be used to invoke a device control application stored in the memory 1005 to implement:
determining at least one neighbor node of a target node, wherein the neighbor node is a node connected with the target node based on at least one type of edge;
calculating at least one first probability that any neighbor node of the target node propagates a target label to the target node based on various types of edges connected with the target node, wherein one type of edge corresponds to one first probability, so as to obtain a first probability set that each neighbor node propagates the target label to the target node based on various types of edges;
determining, based on the first probability set, a first probability vector that the target node receives the target label based on the same type of edge, so as to obtain a first probability vector set that the target node receives the target label based on multiple types of edges;
obtaining a second probability that the target node receives the target label based on the first probability vector set;
and determining the node type of the target node based on the second probability, and outputting the node type of the target node to a user interaction interface of the terminal equipment so as to be displayed to a user.
In some possible embodiments, the processor 1001 is configured to:
acquiring at least one edge attribute of any type of edge connected with the target node by any neighbor node;
determining the attribute weight of each edge attribute in the at least one edge attribute;
and determining the edge weight of the edge of any type based on the attribute weight of each edge attribute, and determining the edge weight as a first probability that any neighbor node propagates a target label to the target node based on the edge of any type, so as to obtain each first probability that any neighbor node propagates the target label to the target node based on each type of edge between the neighbor node and the target node.
In some possible embodiments, the processor 1001 is configured to:
determining at least one first probability corresponding to the edges with the edge types belonging to the same type from the first probability set;
and constructing a first probability vector based on the at least one first probability that the target node receives the target label based on the same type of edge.
In some possible embodiments, the processor 1001 is configured to:
acquiring an association relation between nodes in at least one social network, determining at least one node having an association relation with a target node from the nodes based on the association relation between the nodes, and determining the at least one node as a neighbor node of the target node.
In some possible embodiments, the processor 1001 is configured to:
determining each type weight corresponding to each type in the types of the edges, wherein one type corresponds to one type weight;
determining a second probability that the target node receives the target label based on the respective type weights and the first set of probability vectors.
In some possible embodiments, the processor 1001 is configured to:
comparing the second probability with a preset threshold value;
and when the second probability is greater than or equal to the preset threshold, determining the node type of the target node as the node type marked by the target label.
In some possible embodiments, the neighbor nodes of the target node include a first-degree neighbor node of the target node and a second-degree neighbor node of the target node, where the first-degree neighbor node is a node connected to the target node based on at least one type of edge, and the second-degree neighbor node is any node connected to the target node based on any one-degree neighbor node;
the processor 1001 is further configured to:
determining a second probability vector of the target node receiving the target label propagated from each two-degree neighbor node based on the same type of edge so as to obtain a second probability vector set of the target node receiving the target label propagated from each two-degree neighbor node based on multiple types of edges;
and obtaining a second probability of the target node receiving the target label based on the first probability vector set and the second probability vector set.
It should be understood that, in some possible embodiments, the processor 1001 may be a Central Processing Unit (CPU), and the processor 1001 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 1005 may include both read-only memory and random access memory, and provides instructions and data to the processor 1001. A portion of the memory 1005 may also include non-volatile random access memory. For example, the memory 1005 may also store device type information.
In a specific implementation, the terminal device may execute the implementation manners provided in the steps in fig. 1 to fig. 5 through the built-in functional modules, which may specifically refer to the implementation manners provided in the steps, and are not described herein again.
In the embodiment of the invention, the node relations of different types in different social networks are converted into the node relations of the same type, so that the limitation of the different social networks on the node relations can be eliminated, and the node classes of any node in the different social networks can be predicted. By calculating attribute weights for the respective edge attributes of each type of edge, a respective weight of each edge attribute in its corresponding type of edge may be determined. Therefore, the edge weight of each type of edge can be accurately and reasonably determined based on the attribute weight of each edge attribute, and the applicability is high.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and is executed by a processor to implement the method provided in each step in fig. 1 to 5, which may specifically refer to the implementation manner provided in each step, and is not described herein again.
The computer readable storage medium may be the task processing device provided in any of the foregoing embodiments or an internal storage unit of the foregoing terminal device, such as a hard disk or a memory of an electronic device. The computer readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) card, a flash card (flash card), and the like, which are provided on the electronic device. The computer readable storage medium may further include a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), and the like. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the electronic device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the electronic device. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
The terms "first", "second", and the like in the claims, in the description and in the drawings of the present invention are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments. The term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A method for node class determination, the method comprising:
the method comprises the steps that terminal equipment determines at least one neighbor node of a target node, wherein the neighbor node is a node which is connected with the target node based on at least one type of edge;
the terminal equipment calculates at least one first probability that any neighbor node of the target node propagates a target label to the target node based on various types of edges connected with the target node, wherein one type of edges corresponds to one first probability, so as to obtain a first probability set that each neighbor node propagates the target label to the target node based on various types of edges;
the terminal device determines, based on the first probability set, that the target node receives a first probability vector of the target label based on a same type of edge, so as to obtain a first probability vector set that the target node receives the target label based on multiple types of edges;
the terminal equipment obtains a second probability of receiving the target label by the target node based on the first probability vector set;
and the terminal equipment determines the node type of the target node based on the second probability, and outputs the node type of the target node to a user interaction interface of the terminal equipment so as to be displayed for a user.
2. The method of claim 1, wherein the terminal device calculates at least one first probability that any neighbor node of the target node propagates a target label to the target node based on various types of edges connected to the target node, comprising:
the terminal equipment acquires at least one edge attribute of any type of edge connected with any neighbor node and the target node;
the terminal equipment determines the attribute weight of each edge attribute in the at least one edge attribute;
the terminal device determines an edge weight of the edge of any type based on the attribute weight of each edge attribute, and determines the edge weight as a first probability that any neighbor node propagates a target label to the target node based on the edge of any type, so as to obtain each first probability that any neighbor node propagates the target label to the target node based on each type of edge between the neighbor node and the target node.
3. The method of claim 1, wherein the terminal device determining, based on the first set of probabilities, that the target node receives the first probability vector for the target label based on edges of a same type, comprises:
the terminal equipment determines at least one first probability corresponding to the edges with the edge types belonging to the same type from the first probability set;
the terminal device constructs, based on the at least one first probability, a first probability vector that the target node receives the target label based on the same type of edge.
4. The method according to any of claims 1 to 3, wherein the terminal device determines a plurality of neighbor nodes of a target node, comprising:
the terminal equipment obtains the association relation among all nodes in at least one social network, determines at least one node with the association relation with a target node from all nodes based on the association relation among all nodes, and determines the at least one node as a neighbor node of the target node.
5. The method according to any of claims 1 to 4, wherein the obtaining, by the terminal device, a second probability that the target node receives the target label based on the first set of probability vectors comprises:
the terminal equipment determines each type weight corresponding to each type in the types of the edges, wherein one type corresponds to one type weight;
the terminal device determines a second probability that the target node receives the target label based on the respective type weights and the first set of probability vectors.
6. The method according to any one of claims 1 to 5, wherein the determining, by the terminal device, the node class of the target node based on the second probability comprises:
the terminal equipment compares the second probability with a preset threshold value;
and when the second probability is greater than or equal to the preset threshold, the terminal device determines that the node category of the target node is the node category marked by the target label.
7. The method of claim 1, wherein the neighbor nodes of the target node include a first-degree neighbor node of the target node and a second-degree neighbor node of the target node, the first-degree neighbor node being a node connected to the target node based on at least one type of edge, the second-degree neighbor node being any node connected to the target node based on any one-degree neighbor node;
the method further comprises the following steps:
the terminal equipment determines that the target node receives second probability vectors of the target labels transmitted from all the second-degree neighbor nodes based on the same type of edges so as to obtain a second probability vector set of the target node transmitting the target labels from all the second-degree neighbor nodes based on multiple types of edges;
the terminal equipment obtains a second probability of the target node receiving the target label based on the first probability vector set and the second probability vector set.
8. An apparatus for node class determination, the apparatus comprising:
the node determination module is used for determining at least one neighbor node of a target node, wherein the neighbor node is a node which is connected with the target node based on at least one type of edge;
a first probability determination module, configured to calculate at least one first probability that any neighboring node of the target node propagates a target label to the target node based on various types of edges connected to the target node, where one type of edge corresponds to one first probability, so as to obtain a first set of probabilities that each neighboring node propagates the target label to the target node based on various types of edges;
a probability vector determination module, configured to determine, based on the first set of probabilities, a first probability vector that the target node receives the target label based on a same type of edge, so as to obtain a first set of probability vectors that the target node receives the target label based on multiple types of edges;
a second probability determination module, configured to obtain, based on the first set of probability vectors, a second probability that the target node receives the target label;
and the node type determining module is used for determining the node type of the target node based on the second probability and outputting the node type of the target node to a user interaction interface of the terminal equipment so as to be displayed for a user.
9. A terminal device, comprising: a processor and a memory;
the processor is coupled to a memory, wherein the memory is configured to store program code and the processor is configured to invoke the program code to perform the method of any of claims 1-7.
10. A computer storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions which, when executed by a processor, perform the method according to any one of claims 1-7.
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| CN116089722A (en) * | 2023-02-15 | 2023-05-09 | 北京欧拉认知智能科技有限公司 | Implementation method, device, computing equipment and storage medium based on graph yield label |
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| CN116089722A (en) * | 2023-02-15 | 2023-05-09 | 北京欧拉认知智能科技有限公司 | Implementation method, device, computing equipment and storage medium based on graph yield label |
| CN116089722B (en) * | 2023-02-15 | 2023-11-21 | 北京欧拉认知智能科技有限公司 | Implementation method, device, computing equipment and storage medium based on graph yield label |
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