Crowd attraction-driven community evolution on social network

Fuzhong Nian, Li Luo () and Xuelong Yu ()
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Fuzhong Nian: School of Computer & Communication, Lanzhou University of Technology, Gansu Province, Lanzhou, Gansu 730050, China
Li Luo: School of Computer & Communication, Lanzhou University of Technology, Gansu Province, Lanzhou, Gansu 730050, China
Xuelong Yu: School of Computer & Communication, Lanzhou University of Technology, Gansu Province, Lanzhou, Gansu 730050, China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 01, 1-21

Abstract: 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.

Keywords: Community detection; basic community; information propagation; crowd attraction; large-scale social networks (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122500097

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