CN112966910A - Supply and demand network community structure mining method - Google Patents
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Abstract
The invention discloses a method for excavating a supply and demand network community structure, wherein the method comprises the following steps: dividing a supply and demand network into maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure; combining the maximum groups in each initial community structure to obtain an intermediate community structure; and the isolated nodes in the intermediate community structure are brought into the community network of the intermediate community structure to obtain a target community structure, so that the community structure with better quality is found from all the community structures obtained according to various combination modes, and the nodes except the maximum community are brought into the community network of the intermediate community structure to realize the mining of the community structure with the heterogeneous supply and demand network.
Description
Technical Field
The invention relates to the field of network science, in particular to a heterogeneous supply and demand network community structure mining method and a computer readable storage medium.
Background
In the real world, many networks have a binary structure, that is, the network includes two different types of nodes, there is no connecting edge between the same type of nodes, and there is a connecting edge between the different types of nodes. The supply and demand network is composed of two different types of nodes, namely a supply node and a demand node, and is a typical two-part heterogeneous network, wherein the demand node is closely related to human beings, the supply node refers to a geographic entity providing various urban services, a connection edge represents a connection formed by the demand node and the supply node, and the population flow between the demand node and the supply node is used as a connection edge weight. Typically, residents (demand nodes) enjoy the services of the ecological systems of leisure recreation, tour and sightseeing, entertainment and fitness and the like of the green lands (supply nodes) of the city parks, so as to establish a park service supply and demand network. The community structure is an important characteristic of the supply and demand network, and is different from the homogeneous network in that nodes in the dichotomous heterogeneous network are naturally divided into different types, so that the problems of information loss and redundancy of the original dichotomous heterogeneous network, sharp increase of the number of edges and the like are caused if the dichotomous heterogeneous network is projected into the homogeneous network and then the community structure is excavated, and the accuracy of the excavation result is affected, and therefore a method suitable for excavating the dichotomous heterogeneous network community structure is urgently needed.
Disclosure of Invention
The invention mainly aims to provide a supply and demand network community structure mining method and a computer readable storage medium, aiming at mining a community structure of a city supply and demand network. The mining method of the community structure of the supply and demand network comprises the following steps:
dividing a supply and demand network into maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure;
combining the maximum community in each initial community structure to obtain an intermediate community structure;
and incorporating the isolated node in the intermediate community structure into the community network of the intermediate community structure to obtain a target community structure.
In one embodiment, the aggregation type represents the number of demand nodes and the number of supply nodes in the maximum clique.
In one embodiment, the step of combining the maximum cliques in each of the initial community structures to obtain an intermediate community structure comprises:
in each initial community structure, selecting the demand node and the supply node from the maximum community which is not in the same maximum community, and connecting the demand node and the supply node to obtain at least one second initial community structure;
calculating the modularity of each second initial community structure;
and determining a second initial community structure corresponding to the maximum value in the modularity as an intermediate community structure.
In one embodiment, the second initial community structure includes a set of demand nodes U ═ U (U ═ U)1,u2,…,ui,…,um) And a supply node set V ═ (V)1,v2,…,vj,…,vm) The formula for calculating the modularity of each second initial community structure is as follows:
In one embodiment, the step of incorporating the isolated node in the intermediate community structure into the community network of the intermediate community structure to obtain the target community structure includes:
acquiring a connection edge weight data set of an isolated node in the intermediate community structure and each node in the community network;
determining the community network to which the isolated node belongs according to the maximum value in the continuous edge weight data set;
and incorporating the isolated node into the community network to which the isolated node belongs to obtain a target community structure.
In addition, to achieve the above object, the present invention further provides a supply and demand network community structure mining device, including:
the system comprises a dividing module, a judging module and a judging module, wherein the dividing module is used for dividing a supply and demand network into maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure;
the combination module is used for combining the maximum community in each initial community structure to obtain an intermediate community structure;
and the inclusion module is used for incorporating the isolated nodes in the intermediate community structure into the community network of the intermediate community structure to obtain a target community structure.
In addition, to achieve the above object, the present invention further provides a supply and demand network community structure mining device, which includes a memory, a processor, and a supply and demand network community structure mining program stored on the memory and executable on the processor, wherein the supply and demand network community structure mining program, when executed by the processor, implements the steps of the supply and demand network community structure mining method described above.
In addition, to achieve the above object, the present invention further provides a computer readable storage medium, on which the supply and demand network community structure mining program is stored, and the supply and demand network community structure mining program, when executed by a processor, implements the steps of the supply and demand network community structure mining method as described above.
The method comprises the steps of finding out the maximum groups from a supply and demand network according to the aggregation type of the maximum groups, dividing the maximum groups to obtain first initial community structures, combining the maximum groups in the first initial community structures in various ways to further obtain an intermediate community structure with better quality formed by a plurality of community networks, finding out the community structures with better quality from all the community structures obtained according to various combination ways, and bringing nodes except the maximum groups into the community network with the intermediate community structure, so that the mining of the community structures with the heterogeneous supply and demand network is realized.
Drawings
FIG. 1 is a diagram of a hardware architecture of a device implementing an embodiment of the invention;
FIG. 2 is a flowchart illustrating an embodiment of a method for mining a community structure of a supply and demand network according to the present invention;
FIG. 3 is a schematic diagram of a supply and demand network according to the present invention;
FIG. 4 is a schematic diagram of the maximum clique of the present invention;
FIG. 5 is a diagram illustrating a first initial community structure corresponding to the maximum community k (2,2) according to the present invention;
FIG. 6 is a diagram illustrating a first initial community structure corresponding to the maximum community k (2,3) according to the present invention;
FIG. 7 is a diagram of a second primary community structure of FIG. 6.
The implementation, functional features and advantages of the present invention will be described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a supply and demand network community structure mining device, and referring to fig. 1, fig. 1 is a schematic structural diagram of a hardware operating environment according to an embodiment of the invention.
Fig. 1 is a schematic structural diagram of a hardware operating environment of an excavation device for a supply and demand network community structure. The mining equipment for the community structure of the supply and demand network in the embodiment of the invention can be equipment such as a Personal Computer (PC), a portable Computer, a server and the like.
As shown in fig. 1, the supply and demand network community structure mining apparatus may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, a 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), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a 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 (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Optionally, the supply and demand network community structure excavating equipment may further include an RF (Radio Frequency) circuit, a sensor, a WiFi module, and the like.
Those skilled in the art will appreciate that the supply and demand network community structure excavation equipment structure shown in fig. 1 does not constitute a limitation of the supply and demand network community structure excavation equipment, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a computer storage readable storage medium, may include therein an operating system, a network communication module, a user interface module, and a supply and demand network community structure mining program. The operating system is a program for managing and controlling hardware and software resources of the supply and demand network community structure mining equipment, and supports the operation of the supply and demand network community structure mining program and other software or programs.
The supply and demand network community structure mining equipment shown in fig. 1 is used for mining a community structure of a supply and demand network, and the user interface 1003 is mainly used for detecting or outputting various information, such as an instruction for inputting the mining of the supply and demand network community structure and a result of the mining; the network interface 1004 is mainly used for interacting with a background server and communicating; the processor 1001 may be configured to invoke a supply and demand network community structure mining program stored in the memory 1005 and perform the following operations:
dividing a supply and demand network into maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure;
combining the maximum community in each initial community structure to obtain an intermediate community structure;
and incorporating the isolated node in the intermediate community structure into the community network of the intermediate community structure to obtain a target community structure.
The method comprises the steps of finding out the maximum groups from a supply and demand network according to the aggregation type of the maximum groups, dividing the maximum groups to obtain first initial community structures, combining the maximum groups in the first initial community structures in various ways to further obtain an intermediate community structure with better quality formed by a plurality of community networks, finding out the community structures with better quality from all the community structures obtained according to various combination ways, and incorporating nodes except the maximum groups into the community network in the intermediate community structure, so that the mining of the community structure with the heterogeneous supply and demand network is realized.
The specific implementation of the mobile terminal of the present invention is substantially the same as the following embodiments of the mining method for the community structure of the supply and demand network, and will not be described herein again.
Based on the above structure, the embodiment of the mining method for the supply and demand network community structure is provided.
The invention provides a method for mining a supply and demand network community structure.
Referring to fig. 2, fig. 2 is a flowchart illustrating an embodiment of a mining method for a community structure of a supply and demand network according to the present invention.
In the present embodiment, an embodiment of a supply and demand network community structure mining method is provided, and it should be noted that, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that here.
In this embodiment, the method for mining the community structure of the supply and demand network includes:
step S10, dividing the supply and demand network into the maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure;
the community structure is an attribute of various networks in reality, the whole network is composed of a plurality of communities, each community is a subgraph of the network and is called a community network, and the community network is characterized in that the connection among nodes in the same community network is as close as possible, and the connection among nodes in different community networks is as sparse as possible. The embodiment constructs the supply and demand network of the green space of the city park based on the service function view of the ecosystem, the supply and demand network comprises supply nodes and demand nodes, the connection edges in the supply and demand network only exist between heterogeneous nodes, and the connection edges do not exist between the homogeneous nodes. Fig. 3 is a schematic diagram of a supply and demand network (partially, the actual supply and demand network is more complex), with node a being the demand node and node B being the supply node.
The maximum clique refers to an aggregation clique formed by connecting different demand nodes in the supply and demand network with the same or a plurality of same nodes, the embodiment utilizes a classical maximum clique mining algorithm, namely a BronKerbosch algorithm to obtain the maximum clique in the supply and demand network, the algorithm adopts a similar depth-first search and backtracking mixed method, a recursive backtracking algorithm is taken as a basic form, the method is very efficient in practice, and the embodiment is realized through an igraph module of a Python language and a complex network analysis tool network. The maximum cliques are divided into different aggregation types according to the number of demand nodes or the number of supply nodes, k (a, b) is used for representing the aggregation type of the maximum cliques, wherein a represents the number of demand nodes, and b represents the number of supply nodes, and the aggregation types of the maximum cliques in the graph 4 are k (2,2), k (2,3) and k (3,3) respectively.
The preset aggregation types of the maximum groups are many, and generally include k (2,2), k (2,3), k (2,4) … k (3,2), k (3,3), and k (3,4) …, different aggregation types are obtained by changing the number of demand nodes and the number of supply nodes, and the supply and demand network is maximally divided according to each aggregation type to obtain a first initial community structure, it can be understood that each aggregation type corresponds to one first initial community structure, in this embodiment, the supply and demand network is divided by taking k (2,2) and k (2,3) as examples, fig. 5 is the first initial community structure corresponding to the aggregation type k (2,2), fig. 6 is the first initial community structure corresponding to the aggregation type k (2,3), and the dotted line enclosed in the diagram is the maximum group.
Step S20, combining the maximum community in each initial community structure to obtain an intermediate community structure;
it should be noted that the demand node and the supply node included in the maximum group belong to the same social network, and different maximum groups may belong to the same social network or may not belong to the same social network. Therefore, it is necessary to combine the maximum cliques to further determine whether the maximum cliques belong to the same social network. There are many combinations of the maximum cliques, for example, fig. 5 includes the maximum cliques P1-8, and any two or any several of them can be combined. And combining the maximum groups by each first initial community structure to further obtain an intermediate community structure.
In some specific embodiments, step S20 further includes:
step a, in each initial community structure, selecting the demand node and the supply node from the maximum community which is not in the same maximum community, and connecting the demand node and the supply node to obtain at least one second initial community structure;
step b, calculating the modularity of each second initial community structure;
and c, determining that the second initial community structure corresponding to the maximum value in the modularity is an intermediate community structure.
The embodiment provides a maximum community combination method, where a maximum community in a first initial community structure may or may not be selected to be combined with other maximum communities, the maximum community combination method is to select a demand node from one or more maximum communities to connect with a supply node of other maximum communities, the number of the selected demand nodes and the number of the supply nodes are not limited, and a structure obtained by connecting the demand nodes and the supply nodes in different maximum communities is determined as a second initial community structure. It is understood that there are many combinations of the maximum cliques in each of the first initial community structures, and that a plurality of second initial community structures can be obtained.
The combination of the maximum cliques forms a community network, for example, a supply node B10 in the maximum clique q1 is connected with a demand node A10 in the maximum clique q2, so that a community network comprising the maximum clique q1 and the maximum clique q2 is formed; for example, a supply node B9 in the maximum group q1 is connected with a demand node a10 in q2 in the maximum group, so as to form a community network including the maximum group q1 and the maximum group q2, although the two community networks include the same nodes, the connection modes of the nodes are different and are not completely the same community network, the division effects of the different community networks are different, further, the quality of the whole community structure formed by the different community networks is different, generally, the connection edge between the nodes is more compact, which means that the quality of the community network is better, the density of the connection edge between the nodes in the community structure is measured by the modularity in the embodiment, and regarding the calculation of the modularity, the set of the demand nodes in the second initial structure is set as U (U is the calculation of the modularity)1,u2,…,ui,…,um) The set of supply nodes is V ═ V (V)1,v2,…,vj,…,vm) The formula for calculating the modularity is
Q represents modularity, m represents the total number of demand nodes in the second initial community structure, n represents the total number of supply nodes in the second initial community structure, i represents the ith demand node in the second initial community structure, j represents the jth supply node in the second initial community structure, AijRepresenting a contiguous matrix of UxV, PijThe method comprises the steps that an expected value of a connecting edge between an ith demand node and a jth supply node is shown, delta shows whether the ith demand node and the jth supply node are in the same community network in a second initial community structure or not, and if yes, delta is 1; if not, δ is 0. Generally, when the modularity is between 0.3 and 0.7, it indicates that the quality of the second initial community structure corresponding to the modularity is better. Calculate each of the secondAnd determining the second initial community structure corresponding to the maximum modularity as the intermediate community structure. For example, in fig. 6, the supply node B10 and the demand node a10 are paired to combine the maximum clique q1 and the maximum clique q2 to obtain the community network 1, and the demand node a4 and the supply node B4 are paired to combine the maximum clique q3 and the maximum clique q4 to obtain the community network 2, specifically, referring to fig. 7, which is a second initial community structure (intermediate community structure) corresponding to the maximum modularity.
Step S30, incorporating the isolated node in the intermediate community structure into the community network of the intermediate community structure, to obtain a target community structure.
It is understood that not any node in the supply and demand network may be divided into the maximum groups, and there are nodes outside the maximum groups, that is, isolated nodes, which still exist in the second initial community network and the intermediate community structure, such as the demand node a1 and the supply nodes B1 and B11 in fig. 5, and a1, A3, B1 and B11 in fig. 6, and this embodiment also needs to find an affiliated community network for these isolated nodes, and incorporate the isolated nodes into the already established community network.
In some specific embodiments, step S30 further includes:
step d, acquiring a connection edge weight data set of the isolated node in the intermediate community structure and each node in the community network;
step e, determining the community network to which the isolated node belongs according to the maximum value in the continuous edge weight data set;
and f, bringing the isolated node into the community network to which the isolated node belongs to obtain a target community structure.
The connection weight data set is a data set formed by connection weights between isolated nodes in the intermediate community structure and each node in each community network. Different kinds of supply and demand networks, the connection weight comes from different data, when the supply and demand networks are park green space service networks, the connection weight comes from mobile phone signaling data; when the supply and demand network is a public transport service network, the connection side weight comes from traffic card swiping data and the like, and both the mobile phone signaling data and the traffic card swiping data have the characteristics of large sample size, objective and comprehensive data, no obvious tendency in sampling and stronger space-time continuity. The edge weights are expressed in terms of the population flows formed between the demand nodes and the supply nodes. For example, when the network serves a supply and demand network for a park green space, the population flow of a supply and demand node connection path is simulated by using the mobile phone signaling data of residents, and the flow is used as a connection edge weight. Generally, mobile phone signaling OD data (O is a residence and D is a park) in a 6:00-18:00 time period, namely a park opening time period are selected, population flow between geographic areas corresponding to a supply node and a demand node is counted hour by hour, and a daily average OD flow matrix is used as a connecting weight between the demand node and the supply node. When the isolated node is a supply node, extracting a node with the maximum connecting edge weight with the isolated node from the demand nodes which form the maximum cluster, and incorporating the isolated node into a community network where the node is located; when the isolated node is a demand node, extracting a node with the maximum edge weight with the isolated node from the supply nodes which form the maximum cluster, and incorporating the isolated node into the community network where the node is located.
Taking fig. 7 as an example, if the population flow from the providing node B11 to the requiring node a8 in fig. 7 is greater than the population flow from the providing node B11 to the requiring node a5, B11 is taken into the community network 1 in which a8 is located, and all isolated nodes are taken into the community network, so as to obtain a final community structure, that is, a target community structure.
According to the method, the maximum groups are found out from the supply and demand network according to the aggregation type of the maximum groups, the maximum groups are divided to obtain first initial community structures, the aggregation types of the maximum groups are different, the division results of the supply and demand network are different every time, different first initial community structures are obtained, the maximum groups in the first initial community structures are combined in various modes, an intermediate community structure with better quality formed by a plurality of community networks is further obtained, the community structures with better quality are found out from all the community structures obtained according to various combination modes, nodes except the maximum groups are incorporated into the community networks in the intermediate community structure, and the mining of the community structure with the heterogeneous supply and demand network is realized.
In addition, an embodiment of the present invention further provides an excavation device for a supply and demand network community structure, where the excavation device for a supply and demand network community structure includes:
the system comprises a dividing module, a judging module and a judging module, wherein the dividing module is used for dividing a supply and demand network into maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure;
the combination module is used for combining the maximum community in each initial community structure to obtain an intermediate community structure;
and the inclusion module is used for incorporating the isolated nodes in the intermediate community structure into the community network of the intermediate community structure to obtain a target community structure.
The embodiment of the mining device for the supply and demand network community structure is basically the same as that of the mining method for the supply and demand network community structure, and the details are not repeated here.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a supply and demand network community structure mining program is stored on the computer-readable storage medium, and when executed by a processor, the supply and demand network community structure mining program implements the steps of the supply and demand network community structure mining method described above.
It should be noted that the computer readable storage medium may be provided in the supply and demand network community structure mining device.
The specific implementation manner of the computer-readable storage medium of the present invention is substantially the same as the embodiment of the supply and demand network community structure mining method, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (6)
1. A supply and demand network community structure mining method is characterized by comprising the following steps:
dividing a supply and demand network into maximum groups with the same aggregation type according to at least one preset aggregation type to obtain at least one first initial community structure;
combining the maximum community in each initial community structure to obtain an intermediate community structure;
and incorporating the isolated node in the intermediate community structure into the community network of the intermediate community structure to obtain a target community structure.
2. The supply and demand network community structure mining method according to claim 1, wherein the aggregation type represents the number of demand nodes and the number of supply nodes in the maximum community.
3. The method for mining community structures of supply and demand networks as claimed in claim 2, wherein the step of combining the maximum community in each of the initial community structures to obtain an intermediate community structure comprises:
in each initial community structure, selecting the demand node and the supply node from the maximum community which is not in the same maximum community, and connecting the demand node and the supply node to obtain at least one second initial community structure;
calculating the modularity of each second initial community structure;
and determining a second initial community structure corresponding to the maximum value in the modularity as an intermediate community structure.
4. The supply and demand network community structure mining method of claim 3, wherein the second initial community structure comprises a demand node set U ═ U (U ═ U)1,u2,…,ui,…,um) And a supply node set V ═ (V)1,v2,…,vj,…,vm) The formula for calculating the modularity of each second initial community structure is as follows:
5. The method for mining community structures of supply and demand networks as claimed in claim 2, wherein the step of bringing isolated nodes in the intermediate community structure into the community network of the intermediate community structure to obtain a target community structure comprises:
acquiring a connection edge weight data set of an isolated node in the intermediate community structure and each node in the community network;
determining the community network to which the isolated node belongs according to the maximum value in the continuous edge weight data set;
and incorporating the isolated node into the community network to which the isolated node belongs to obtain a target community structure.
6. A computer-readable storage medium, wherein a supply and demand network community structure mining program is stored on the computer-readable storage medium, and when executed by a processor, implements the steps of the supply and demand network community structure mining method according to any one of claims 1 to 5.
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