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Multi-objective optimization community discovery system and method based on dynamic social network attributes

A multi-objective optimization, social network technology, applied in instruments, data processing applications, computing, etc., can solve the problems of random convergence direction, slow convergence speed, and high computing cost

Inactive Publication Date: 2020-10-30
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Nevertheless, the randomness of the initialization of the evolutionary algorithm leads to the slowness of the convergence rate and the randomness of the convergence direction
Therefore multiple iterations are required to obtain good partition results, and these methods are not ideal if multidimensional features in dynamic communities are considered; thus, computational cost is high when evolutionary algorithms are used for community detection

Method used

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  • Multi-objective optimization community discovery system and method based on dynamic social network attributes
  • Multi-objective optimization community discovery system and method based on dynamic social network attributes
  • Multi-objective optimization community discovery system and method based on dynamic social network attributes

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0064] In this example, see figure 1 and Figure 12 , a multi-objective optimization community discovery computing model based on dynamic social network attributes, comprising a receiving unit 2, a data preprocessing unit 3, a computing unit 4, a display unit 5 and a main control unit 1, characterized in that: the receiving unit 2 , data preprocessing unit 3, computing unit 4, and display unit 5 are sequentially connected through the data bus, the main control unit 1 is respectively connected to the receiving unit 2, the data preprocessing unit 3, the computing unit 4, and the display unit 5, and the receiving unit 2, The data preprocessing unit 3, the calculation unit 4, and the display unit 5 are also respectively connected to the main control unit 1 through a signal bus;

[0065] The receiving unit 2 is used to receive and store dynamic social network data;

[0066] The data preprocessing unit (3) is used to number and convert the received dynamic social network data into...

Embodiment 2

[0071] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0072] In this embodiment, the graph data structure is expressed as G={G 1 ,G 2 ,...,G t}, where G represents a dynamic network graph, G t =(V t ,E t ) represents the snapshot graph at time step t, V t Represents the collection of nodes in time step t, each node represents a user or a group, E t Represents the set of edges between nodes in time step t, and each edge represents the relationship or interaction between two nodes.

[0073] In this example, the defined probability selection method. During the initialization of the algorithm, a set of proposals of a specified size is generated. Each scheme is a set of community connection key-value pairs contained in each node in the network. This paper proposes a probabilistic selection method to select the community connection key value of each node during initialization. the key y of the i-th node i According to the...

Embodiment 3

[0092] This embodiment is basically the same as the foregoing embodiment, and the special features are as follows:

[0093] In this example, see Figure 12 , a multi-objective optimization community discovery method based on dynamic social network attributes, using the above system to operate, is characterized in that it includes the following steps:

[0094] S1. Obtain dynamic social network data through the receiving unit, and then abstract the social network into a graph data structure through the data preprocessing unit as G={G 1 ,G 2 ,...,G t}, where G represents a dynamic network graph, G t =(V t ,E t ) represents the snapshot graph at time step t, V t Represents the collection of nodes in time step t, each node represents a user or a group, E t Represents the set of edges between nodes in time step t, and each edge represents the relationship or interaction between two nodes;

[0095] S2. In the current time step, encode the N schemes in the scheme set according...

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Abstract

The invention discloses a multi-objective optimization community discovery system and method based on dynamic social network attributes. The system comprises a receiving unit, a data preprocessing unit, a computing unit, a display unit and a main control unit. The method comprises the following steps: abstracting a dynamic network into a dynamic network diagram formed by combining networks of T time steps, and dividing the network of the current time step t into K communities, so that a network division result is kept continuous. The invention provides a multi-objective optimization method tosolve the problem of dynamic network community division. The multi-objective optimization method comprises an initialization part and an iteration part. According to the method, a probability selection method is provided to initialize a scheme and generate an initial scheme set, a structure evolution adaptive model is further provided and comprises community structure quality and community evolution continuity, and Pt quality and evolution continuity between Pt-1 and Pt are guaranteed by using different evaluation functions. The invention provides an objective function dynamic evolution continuity measurement for evaluating community evolution continuity.

Description

technical field [0001] The invention relates to the field of social network research, in particular to a multi-objective optimization community discovery system and method based on dynamic social network attributes. Background technique [0002] In recent years, due to the rapid development of social networks, the study of social networks has become more and more important. Since the structure of real-world networks changes over time, networks are not static. As in scientific coauthoring networks, scientists align their research direction and publish papers with different coauthors. In online networking, interactions among friends create distinct circles of friends. These features indicate the non-static nature of the network structure. [0003] Some recent studies unify the process of community detection and evolution based on the evolutionary clustering concept devised by Chakrabarti et al. Methods based on evolutionary clustering are also known as temporal smoothing f...

Claims

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Application Information

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IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 李卫民范钰婷刘炜戴东波
Owner SHANGHAI UNIV
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