A genetic algorithm approach for detecting hierarchical and overlapping community structure in dynamic social networks

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

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Crowd attraction-driven community evolution on social network

International Journal of Modern Physics C ◽

10.1142/s0129183122500097 ◽

2021 ◽

pp. 2250009

Author(s):

Fuzhong Nian ◽

Li Luo ◽

Xuelong Yu

Keyword(s):

Social Networks ◽

Community Structure ◽

Social Network ◽

Large Scale ◽

Detection Algorithm ◽

Information Propagation ◽

Evolution Analysis ◽

Dynamic Social Networks ◽

Community Evolution ◽

Community Detection Algorithm

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

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A Novel Framework for Analyzing Overlapping Community Evolution in Dynamic Social Networks

Communications in Computer and Information Science - Geo-Spatial Knowledge and Intelligence ◽

10.1007/978-981-10-3966-9_7 ◽

2017 ◽

pp. 60-70

Author(s):

Hui Jiang ◽

Xiaolong Xu ◽

Jiaying Wu ◽

Xuewu Zhang

Keyword(s):

Social Networks ◽

Dynamic Social Networks ◽

Community Evolution ◽

Overlapping Community

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Overlapping Community Discovery Algorithm Based on Hierarchical Agglomerative Clustering

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418500088 ◽

2017 ◽

Vol 32 (03) ◽

pp. 1850008 ◽

Cited By ~ 6

Author(s):

Hongtao Liu ◽

Linghu Fen ◽

Jie Jian ◽

Long Chen

Keyword(s):

Social Networks ◽

Community Structure ◽

The Other ◽

Node Degree ◽

Agglomerative Clustering ◽

Community Discovery ◽

The Core ◽

Degree Of Membership ◽

Hierarchical Agglomerative Clustering ◽

Overlapping Community

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable.

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