Identifying influential individuals in microblogging networks using graph partitioning

Identifying influential individuals who lead to faster and wider spreading of influence in social networks is of theoretical significance and practical value to either accelerating the speed of propagation in the case of product promotion, or hindering the pace of diffusion involved in rumors. Conventional methods, ranging from centrality indices to diffusion-based processes, already take into account the number and influences of followers, but fail to make full use of the characteristics of social media. A novel approach called PartitionRank for finding a pre-fixed number of influential individuals in microblogging scenarios is proposed in this study to maximize the impact; it combines interest similarity with social interaction between users via graph partitioning. Experimental results on artificial and real-world microblogging networks illustrate that our scheme outperforms the other state-of-the-art methods in effectiveness and efficiency.