Influence maximization algorithm based on community structure

Wei Liu; Can Zhang; Meng He

doi:10.1117/12.2639185

15 July 2022 Influence maximization algorithm based on community structure

Wei Liu, Can Zhang, Meng He

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 1225817 (2022) https://doi.org/10.1117/12.2639185
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

In recent years, how to identify the most influential nodes has become the forefront of network science. Considering the influence of community structure and neighbor nodes within the second order on node influence diffusion, this paper proposes an influence maximization algorithm based on community structure (IMCS). Firstly, the CPM algorithm is used to detect the community of the network to obtain the community structure of network. Then, select the nodes belonging to multiple communities in the community and some potential nodes in each community to form a candidate node set. Finally, use the improved Prob-Degree algorithm to screen all seed nodes. Experimental data show that compared with Prob-degree, CoFIM and DD, the algorithm proposed in this paper has relatively good overall performance in Oregon network, and there are seed node intervals with relatively good performance in other networks too.

Citation Download Citation

Wei Liu, Can Zhang, and Meng He "Influence maximization algorithm based on community structure", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 1225817 (15 July 2022); https://doi.org/10.1117/12.2639185

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