Detecting the evolving community structure in dynamic social networks

2019 ◽  
Vol 23 (2) ◽  
pp. 715-733 ◽  
Author(s):  
Fanzhen Liu ◽  
Jia Wu ◽  
Shan Xue ◽  
Chuan Zhou ◽  
Jian Yang ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Dynamic Social Networks

scholarly journals Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
Jingjing Ma ◽  
Jie Liu ◽  
Wenping Ma ◽  
Maoguo Gong ◽  
Licheng Jiao
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Evolutionary Algorithm ◽  
Community Detection ◽  
Dynamic Networks ◽  
Time Step ◽  
Current Time ◽  
Multiobjective Evolutionary Algorithm ◽  
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.

Crowd attraction-driven community evolution on social network

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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