Identify Influential Spreaders in Online Social Networks Based on Social Meta Path and PageRank
Identifying “influential spreader” is finding a subset of individuals in the social network, so that when information injected into this subset, it is spread most broadly to the rest of the network individuals. The determination of the information influence degree of individual plays an important role in online social networking. Once there is a list of individuals who have high influence, the marketers can access these individuals and seek them to impress, bribe or somehow make them spread up the good information for their business as well as their product in marketing campaign. In this paper, according to the idea “Information can be spread between two unconnected users in the network as long as they both check-in at the same location”, we proposed an algorithm called SMPRank (Social Meta Path Rank) to identify individuals with the largest influence in complex online social networks. The experimental results show that SMPRank performs better than Weighted LeaderRank because of the ability to determinate more influential spreaders.
KeywordsInfluential spreader LeaderRank Random walk PageRank Social meta path
- 3.Zhan, Q., Zhang, J., Wang, S., Yu, P.S., Xie, J.: Influence maximization across partially aligned heterogenous social networks. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS, vol. 9077, pp. 58–69. Springer, Heidelberg (2015)Google Scholar
- 12.Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on Twitter based on temporal and social terms evaluation. In: Proceedings of the Tenth International Workshop on Multimedia Data Mining, MDMKDD 2010, pp. 4–13 (2010)Google Scholar
- 13.Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. In: WWW 1998, pp. 161–172 (1998)Google Scholar
- 14.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, KDD 2003, pp. 137–146. ACM, New York (2003)Google Scholar