CN113011471A - Social group dividing method, social group dividing system and related devices - Google Patents
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Abstract
The application provides a social group dividing method, which comprises the following steps: acquiring social data and clustering requirements, and determining a network structure and node information corresponding to the social data; carrying out random walk according to the network structure and the node information to obtain a social network diagram; and carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement. According to the method, a simple random walk mechanism is adopted, all nodes are used as initial nodes to carry out random walk to form a new social network diagram, the reliability of the social network is increased to a certain extent, and meanwhile, a group with strong influence is favorably divided. The method and the device are simple and convenient, are easy to simulate in a software mode, realize the division of different social groups, and meet the social network structure of real life. The application also provides a social group dividing system, a computer readable storage medium and an electronic device, which have the beneficial effects.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a social group partitioning method, a social group partitioning system, and a related device.
Background
With the development of internet technology, online social networks are developed explosively, and people's lives are influenced and changed by the online social networks even though the online social networks are not separated. The intensive research on the spreading mode of the influence helps to understand the behaviors of human groups and individuals, so that the prediction on the behaviors of people is made, and reliable basis and suggestion are provided for the decision of departments such as governments, enterprises and the like.
Clustering in social groups is a process of dividing data samples into groups of similar objects. Each group is called a cluster, and the similarity of data objects in each cluster is large, while the similarity of objects in different clusters is small. Aiming at the social network under the actual condition, the social network clustering can divide the nodes into different clusters according to the specific position information of the nodes in the network under the actual condition, and different organization clusters hidden in the social network structure under the actual condition are shown, so that the mining and analyzing capability of the social network data is improved.
The traditional social network partitioning method only describes the information propagation and diffusion process in the social network to a certain extent, but due to lack of early preprocessing, the propagation path with small influence is still calculated, the accuracy of different social network partitioning is influenced, and the clustering effect is not obvious.
Disclosure of Invention
The application aims to provide a social group dividing method, a social group dividing system, a computer-readable storage medium and electronic equipment, and the social network reliability is improved by clustering a social network graph obtained by random walk.
In order to solve the technical problem, the application provides a social group partitioning method, which comprises the following specific technical scheme:
acquiring social data and clustering requirements, and determining a network structure and node information corresponding to the social data;
carrying out random walk according to the network structure and the node information to obtain a social network diagram;
and carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
Optionally, performing random walk according to the network structure and the node information, and obtaining the social network diagram includes:
starting from each node in the network structure, performing random walk with preset times and preset steps, and recording the walk path of the random walk;
and selecting a frequent item set in the walking path by using an Apriori algorithm to obtain the social network diagram.
Optionally, starting from each node in the network structure, before performing random walk of the preset number of times and the preset number of steps, the method further includes:
determining the random walk probability of the node by using the probability transfer matrix;
the probability transition matrix is
WijAs the weight of the connecting edge between node i and node j, WigIs the ith row sum of the network weight matrix;
starting from each node in the network structure, the performing random walk of the preset number of times and the preset number of steps includes:
and starting from each node in the network structure according to the random walk probability, and performing random walk for preset times and preset steps.
Optionally, before determining the random walk probability of the node by using the probability transition matrix, the method further includes:
and determining the network weight matrix corresponding to the social network graph according to the node information and the network structure.
Optionally, performing binary clustering on the nodes in the social network diagram by using a preset clustering method includes:
and performing binary clustering on the nodes in the social network graph by using a Kernighan-Lin algorithm or a spectrum bisection method.
Optionally, if the preset clustering method Kernighan-Lin algorithm is used, performing binary clustering on the nodes in the social network graph by using the preset clustering method to obtain the social group meeting the clustering requirement includes:
randomly dividing the social network graph into two subgraphs, taking a node from each of the two subgraphs for exchange, and calculating the difference of gain functions before and after the node exchange; the gain function is the difference between the number of edges in the two subgraphs and the number of edges between the two subgraphs;
exchanging two nodes when the difference value of the gain function is maximum, and exchanging each node in the two subgraphs at most once in each iteration process;
repeatedly exchanging the rest nodes until the difference value of the gain function is smaller than zero or all the nodes in the subgraph are exchanged once, so as to obtain two subgraphs after the first iteration;
judging whether the current two subgraphs meet the clustering requirement;
if yes, taking the current two subgraphs as social groups meeting the clustering requirements;
if not, repeating the iteration until two subgraphs meeting the clustering requirement are obtained.
The present application further provides a social group partitioning system, including:
the data acquisition module is used for acquiring social data and clustering requirements and determining a network structure and node information corresponding to the social data;
the social network confirming module is used for carrying out random walk according to the network structure and the node information to obtain a social network graph;
and the clustering module is used for carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
Optionally, the social network confirming module includes:
a walking unit, configured to perform random walking for a preset number of times and a preset number of steps from each node in the network structure, and record a walking path of the random walking;
and the social network generating unit is used for selecting the frequent item set in the walking path by using an Apriori algorithm to obtain the social network diagram.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method as set forth above.
The present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when calling the computer program in the memory.
The application provides a social group dividing method, which comprises the following steps: acquiring social data and clustering requirements, and determining a network structure and node information corresponding to the social data; carrying out random walk according to the network structure and the node information to obtain a social network diagram; and carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
According to the method, a simple random walk mechanism is adopted, all nodes are used as initial nodes to carry out random walk to form a new social network diagram, the reliability of the social network is increased to a certain extent, and meanwhile, a group with strong influence is favorably divided. The method is simple and convenient, under the support of the current big data technology, the simulation is easily carried out through a software mode, the division of different social groups is realized, the social network structure in real life is met, and certain practical significance is achieved.
The application also provides a social group dividing system, a computer readable storage medium and an electronic device, which have the beneficial effects and are not repeated herein.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a social group partitioning method provided in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a social group partitioning system according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.
Referring to fig. 1, fig. 1 is a flowchart of a social group partitioning method provided in an embodiment of the present application, where the method includes:
s101: acquiring social data and clustering requirements, and determining a network structure and node information corresponding to the social data;
this step is intended to obtain social data, which refers to raw social network data, which may contain user information, and communication information associated with the user presence, which may be presented in the form of a communication record, and clustering requirements. The communication information usually has a corresponding communication target, so that communication between the users is formed, and the connection between the users is indicated in the social network. It should be noted that the communication between the users has directionality, that is, the communication between the user a and the user B, and the communication process between the user B and the user a are two communication processes, each of which includes communication attribute information such as communication frequency, and the communication attribute information can be regarded as influence of the user on another user. In a social network, each user is generally regarded as a node, and an influence relationship of a node on another node can be obtained.
The clustering requirement refers to a clustering standard for social network data, and specific contents of the clustering requirement are not limited herein, and may be parameters such as community density and community quality. A community refers to each class in a social network. The better the community division, the more the edges inside the community are as much as possible, and the fewer the edges between the communities are, i.e. the smaller the intersection between the classes, the better the clustering effect is. Those skilled in the art can determine the clustering requirements based on actual clustering requirements. Of course, the clustering requirements may also be the clustering requirements such as modularity, and are not limited herein.
Since social data is social network data, which typically includes a set of points and a set of edges, a network structure can be determined from the set of points and the set of edges. And node information refers to user information for each user in the social data.
In other words, this step is a process of obtaining a weighted directed graph according to social data. If the social network is represented as a weighted directed graph G, (V, E), where V E V is the set of nodes,
is a collection of directed edges. Each node V ∈ V represents a user in the social network, and each edge (u, V) ∈ E represents the influence relationship of the node u to the node V. The edges are directional, i.e., the influence is directional, node u has influence on node v, but node v may have no influence on node u. The weight of an edge represents the magnitude of the influence.
S102: carrying out random walk according to the network structure and the node information to obtain a social network diagram;
this step is intended to perform a random walk, resulting in a social network diagram. The random walk may start from any node in the network structure determined in the previous step. The number of random walks and the number of steps are not limited herein. The number of times and the number of steps of the random walk may be preset before the step, or may be calculated before the random walk by using a matrix or a function.
Preferably, this step may be performed by the following steps:
starting from each node in the network structure, performing random walk of preset times and preset steps, and recording a walk path of the random walk;
and secondly, selecting a frequent item set in the walking path by using an Apriori algorithm to obtain the social network diagram.
Apriori is a commonly used algorithm for mining data association rules, which is used to find frequently occurring data sets in data values. Of course, Apriori algorithm is preferably adopted to select the frequent item set in this embodiment, and those skilled in the art may also adopt other algorithms to select the frequent item set to obtain the social network diagram. Such as Apriori algorithm-AprioriTid algorithm, which is an optimization algorithm of Apriori algorithm. The Apriori algorithm quantifies the frequent item set and the association rules with support and confidence, which mines the frequent item set through two phases of candidate set generation and downward closed-check detection of episodes. The mining result of the Apriori algorithm has universality, strong confidence and reliability, simple algorithm and low requirement on data of social data.
In addition, from each node in the network structure, before random walk of preset times and preset steps is performed, probability of random walk can be calculated. Specifically, the probability transition matrix may be used to determine the random walk probability of the node.
The probability transfer matrix is
WijAs the weight of the connecting edge between node i and node j, WigIs the sum of the ith row of the network weight matrix. A network weight matrix corresponding to the social network graph may be determined according to the node information and the network structure. The network weight matrix comprises weights of edges in the social network graph and is used for indicating communication conditions among nodes.
If the random walk frequency is calculated first, random walks of preset times and preset steps can be performed from each node in the network structure according to the random walk probability. It should be noted that the preset number refers to the number of times each node performs random walks. If the number of random walks is m, if the network has n nodes, the number of all paths formed by the step is m × n.
S103: and carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
After the social network diagram is obtained, clustering can be performed by using a preset clustering method to obtain a social group meeting the clustering requirement.
The clustering method adopted in the embodiment is not limited, and Kernighan-Lin algorithm or spectrum bisection method can be used for performing dichotomous clustering on the nodes in the social network diagram. No matter what kind of binary clustering method is adopted, since all the nodes are taken as initial nodes to carry out random walk in the embodiment, a new social network diagram is formed, the reliability of the social network is increased to a certain extent, and meanwhile, the division of the group with strong influence is facilitated.
The following description will be given of a specific process of performing binary clustering on nodes in a social network graph by using a preset clustering method to obtain a social group meeting the clustering requirements, by taking a preset clustering method Kernighan-Lin algorithm as an example:
the method comprises the steps that firstly, the social network graph is randomly divided into two subgraphs, a node is taken from each of the two subgraphs to be exchanged, and the difference value of gain functions before and after the node exchange is calculated; the gain function is the difference between the number of edges in the two subgraphs and the number of edges between the two subgraphs;
secondly, exchanging two nodes when the difference value of the gain function is maximum, wherein each node in the two subgraphs is exchanged at most once in each iteration process;
thirdly, repeatedly exchanging the rest nodes until the difference value of the gain function is smaller than zero or all the nodes in the subgraph are exchanged once, so as to obtain two subgraphs after the first iteration;
fourthly, judging whether the current two sub-graphs meet the clustering requirement; if yes, entering the fifth step; if not, entering the sixth step;
fifthly, taking the current two subgraphs as social groups meeting the clustering requirements;
and sixthly, repeating iteration until two sub-graphs meeting the clustering requirement are obtained.
In particular, the social network diagram is randomly divided into two subgraphs K of known size1、K2And defining a gain function, wherein Q is the number of edges in two communities and the number of edges between the communities, and each subgraph is equivalent to one community. And taking one node from each of the two subgraphs to prepare for switching, trying to switch and calculating delta Q (Q switching is followed by Q switching), and selecting a pair of node pairs which enables the delta Q to be maximum for switching. Each node can only be switched once.
Repeating the previous step for the rest nodes until delta Q<0, or until all nodes of a certain subgraph have been swapped once. A second swap of each node is allowed, starting a new iteration until no node pairs can be swapped. At this time, the original social network diagram is divided into two subgraphs K1'、K'2. The similarity between nodes in the same subgraph is large, and the similarity between nodes in different subgraphs is small.
Continue to pair subgraph K using the same method according to clustering requirements1'、K'2And carrying out clustering division until the clustering requirement is met.
The following describes a social group partitioning system provided in an embodiment of the present application, and the below-described partitioning system and the above-described social group partitioning method may be referred to in correspondence with each other.
Fig. 2 is a schematic structural diagram of a social group partitioning system provided in an embodiment of the present application, and the present application further provides a social group partitioning system, including:
the data acquisition module 100 is configured to acquire social data and a clustering requirement, and determine a network structure and node information corresponding to the social data;
the social network confirming module 200 is configured to perform random walk according to the network structure and the node information to obtain a social network diagram;
the clustering module 300 is configured to perform binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
Based on the above embodiment, as a preferred embodiment, the social network confirming module 200 includes:
a walking unit, configured to perform random walking for a preset number of times and a preset number of steps from each node in the network structure, and record a walking path of the random walking;
and the social network generating unit is used for selecting the frequent item set in the walking path by using an Apriori algorithm to obtain the social network diagram.
Based on the above embodiment, as a preferred embodiment, the method further includes:
the probability confirmation module is used for determining the random walk probability of the node by utilizing the probability transfer matrix; the probability transition matrix is
WijAs the weight of the connecting edge between node i and node j, WigIs the ith row sum of the network weight matrix;
the walking unit is a unit configured to perform random walking for a preset number of times and a preset number of steps from each node in the network structure according to the random walking probability.
Based on the above embodiment, as a preferred embodiment, the method further includes:
and the weight confirmation module is used for determining the network weight matrix corresponding to the social network graph according to the node information and the network structure.
Based on the above embodiment, as a preferred embodiment, the clustering module 300 includes:
and the clustering unit is used for performing binary clustering on the nodes in the social network diagram by utilizing a Kernighan-Lin algorithm or a spectrum bisection method.
Based on the above embodiment, as a preferred embodiment, if the preset clustering method Kernighan-Lin algorithm is used, the clustering module 300 is a module for executing the following steps:
randomly dividing the social network graph into two subgraphs, taking a node from each of the two subgraphs for exchange, and calculating the difference of gain functions before and after the node exchange; the gain function is the difference between the number of edges in the two subgraphs and the number of edges between the two subgraphs; exchanging two nodes when the difference value of the gain function is maximum, and exchanging each node in the two subgraphs at most once in each iteration process; repeatedly exchanging the rest nodes until the difference value of the gain function is smaller than zero or all the nodes in the subgraph are exchanged once, so as to obtain two subgraphs after the first iteration; judging whether the current two subgraphs meet the clustering requirement; if yes, taking the current two subgraphs as social groups meeting the clustering requirements; if not, repeating the iteration until two subgraphs meeting the clustering requirement are obtained.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed, may implement the steps provided by the above-described embodiments. The storage medium may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The application further provides an electronic device, which may include a memory and a processor, where the memory stores a computer program, and the processor may implement the steps provided by the foregoing embodiments when calling the computer program in the memory. Of course, the electronic device may also include various network interfaces, power supplies, and the like.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system provided by the embodiment, the description is relatively simple because the system corresponds to the method provided by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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.
Claims (10)
1. A social group partitioning method is characterized by comprising the following steps:
acquiring social data and clustering requirements, and determining a network structure and node information corresponding to the social data;
carrying out random walk according to the network structure and the node information to obtain a social network diagram;
and carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
2. The method of claim 1, wherein the step of randomly walking according to the network structure and the node information to obtain a social network graph comprises:
starting from each node in the network structure, performing random walk with preset times and preset steps, and recording the walk path of the random walk;
and selecting a frequent item set in the walking path by using an Apriori algorithm to obtain the social network diagram.
3. The method for dividing a social group according to claim 2, wherein before the random walk of the preset number of times and the preset number of steps is performed from each node in the network structure, the method further comprises:
determining the random walk probability of the node by using the probability transfer matrix;
the probability transition matrix is
WijAs the weight of the connecting edge between node i and node j, WigIs the ith row sum of the network weight matrix;
starting from each node in the network structure, the performing random walk of the preset number of times and the preset number of steps includes:
and starting from each node in the network structure according to the random walk probability, and performing random walk for preset times and preset steps.
4. The method for partitioning social groups according to claim 3, wherein before determining the random walk probability of the node by using the probability transition matrix, the method further comprises:
and determining the network weight matrix corresponding to the social network graph according to the node information and the network structure.
5. The method for partitioning social groups according to claim 1, wherein the performing dichotomy clustering on the nodes in the social network graph by using the preset clustering method comprises:
and performing binary clustering on the nodes in the social network graph by using a Kernighan-Lin algorithm or a spectrum bisection method.
6. The method for dividing the social group according to claim 1, wherein if the preset clustering method Kernighan-Lin algorithm is used, performing dichotomy clustering on the nodes in the social network graph by using a preset clustering method to obtain the social group meeting the clustering requirement comprises:
randomly dividing the social network graph into two subgraphs, taking a node from each of the two subgraphs for exchange, and calculating the difference of gain functions before and after the node exchange; the gain function is the difference between the number of edges in the two subgraphs and the number of edges between the two subgraphs;
exchanging two nodes when the difference value of the gain function is maximum, and exchanging each node in the two subgraphs at most once in each iteration process;
repeatedly exchanging the rest nodes until the difference value of the gain function is smaller than zero or all the nodes in the subgraph are exchanged once, so as to obtain two subgraphs after the first iteration;
judging whether the current two subgraphs meet the clustering requirement;
if yes, taking the current two subgraphs as social groups meeting the clustering requirements;
if not, repeating the iteration until two subgraphs meeting the clustering requirement are obtained.
7. A system for partitioning a social group, comprising:
the data acquisition module is used for acquiring social data and clustering requirements and determining a network structure and node information corresponding to the social data;
the social network confirming module is used for carrying out random walk according to the network structure and the node information to obtain a social network graph;
and the clustering module is used for carrying out binary clustering on the nodes in the social network graph by using a preset clustering method to obtain a social group meeting the clustering requirement.
8. The system for partitioning a social group according to claim 7, wherein the social network confirming module comprises:
a walking unit, configured to perform random walking for a preset number of times and a preset number of steps from each node in the network structure, and record a walking path of the random walking;
and the social network generating unit is used for selecting the frequent item set in the walking path by using an Apriori algorithm to obtain the social network diagram.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of dividing a social group according to any one of claims 1 to 6.
10. An electronic device, characterized in that it comprises a memory in which a computer program is stored and a processor which, when it is called in said memory, implements the steps of the method of partitioning social groups according to any one of claims 1 to 6.
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| PCT/CN2022/074604 WO2022179384A1 (en) | 2021-02-26 | 2022-01-28 | Social group division method and division system, and related apparatuses |
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