The evolving network model with community size and distance preferences

Hailiang Chen, Bin Chen, Chuan Ai, Mengna Zhu and Xiaogang Qiu

Physica A: Statistical Mechanics and its Applications, 2022, vol. 596, issue C

Abstract: With the development of network models, the importance of community structure had caught much attention. How can the community size and the community distance affect the network structure remains unexplored. Therefore, in this paper, the MoncSid-N and the MoncSid-E are proposed in response to the issue The community size and distance preferences are introduced in these two models. The networks generated by the MoncSid-N show a better similarity to the real-world networks. The network metrics, including average degree, distribution of node degree, and distribution of community size, are used to analyze the performance of the MoncSid-N. Meanwhile, the MoncSid-E solves the problems of the evolution of large-scale networks. A parallel implementation by Pregel of the MoncSid-E is proposed. It is shown that the network with millions of nodes can be generated by the MoncSid-E efficiently. Based on the plenty of simulations and the comparison of real-world networks, the performances of the MoncSid-N and the MoncSid-E are testified.

Keywords: Evolving network; Community structure; Community distance; Community size (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001418

DOI: 10.1016/j.physa.2022.127112

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