An Algorithm of Influence Maximization in Social Network Based on Local Structure Characteristics
Social network influence maximization algorithm can be widely applied in social marketing, public sentiment control and other relevant fields. This paper focuses on two major problems local structure characteristics of the network which was ignored by previous work and the nodes influence overlapping problems. To deal with the problems, we propose a new influence maximization algorithm for social networks based on local structure characteristics. In this algorithm, the topological potential is used to measure the influence of the nodes to divide communities. The candidate global influence nodes are selected from the community according to the measurements then the local edge deduplication is applied to complete the seed nodes selection. Finally, we conduct extensive experiments to evaluate the feasibility and effectiveness of the algorithm, which, as the results, has better performance in propagation effect compared with the state of the arts.
KeywordsSocial network Community discovery Influence maximization Local edge deduplication
This work was supported by The Youth Foundation of Heilongjiang Province of China under Grant No. QC2016083, the Innovative Talents Research Special Funds of Harbin Science and Technology Bureau under Grant No. 2016RQQXJ128, The Fundamental Research Funds for the Central Universities under Grant No. HEUCF180606, and the National Natural Science Foundation of China under Grant No. 61672179, 61370083 and 61402126.
- 1.Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, San Francisco, America, pp. 57–66 (2001)Google Scholar
- 2.Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, Washington, America, pp. 137–146 (2003)Google Scholar
- 4.Nicosia, V., Criado, R., Romance, M., Russo, G., Latora, V.: Controlling centrality in complex networks. Sci. Rep. 2(1), 230 (2011)Google Scholar
- 5.Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. In: International Conference on World Wide Web 1998, vol. 56, pp. 107–117. Elsevier, Amsterdam (1998)Google Scholar
- 8.Gou, C.C., Du, P., He, M., Liu, Y., Cheng, X.Q.: Tsk-shell: an algorithm for finding topic-sensitive influential spreaders. J. Comput. Res. Develop. 54(2), 361–368 (2017)Google Scholar
- 9.Zhao, F.Q., Yang, G.J., Wang, S.L., He, L.: Application research of algebraic connectivity in influence propagation maximization of social network. Appl. Res. Comput. 35(1), 177–181 (2018)Google Scholar