Abstract
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. In large social networks, nodes are influenced by others for various reasons. Understanding influence can benefit various applications such as viral marketing, recommendation, and information retrieval. However, most existing works on social influence analysis have focused on verifying the existence of social influence. In this paper, an algorithm is proposed which utilizes the heterogeneous link information and the textual content associated with each node in the network to mine micro and macro influence. Based on the direct and indirect influence, the MapReduce framework for social influence analysis algorithm is proposed to derive the indirect influence between nodes. We further study how the discovered topic-level influence can help the prediction of user behaviors. Empirical studies on a large real-world mobile social network show that our algorithm has a good scalability performance.
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