Social influence occurs when one's opinions, emotions, or behaviors are affected by others, intentionally or unintentionally. There are three major research topics in social influence: test, measure, and model.
Our research mainly focuses on quantifying the influential strength between users in large social networks. We try to answer several challenging questions: (1) How to differentiate the social influences from different angles(topics)? (2) How to quantify the strength of those social influences? (3) How to estimate the model on real large networks?
We propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks (Tang et al., KDD'09) and investigate a new problem of conformity influence analysis (Tang et al., KDD'13). We also study the conservative and non-conservative influence propagation over heterogeneous networks (Liu et al., DMKD'12) and propose the notion of social influence locality for modeling retweeting behaviors (Zhang et al., IJCAI'13). We further propose a NTT-FGM model to formalize social influence, correlation (homophily), and users' action dependency into a unified approach and distinguish their effects for modeling and predicting users' actions in social networks (Tan et al., KDD'10). And apply social influence for analyzing user-level sentiment in social networks (Tan et al., KDD'11).
Related data sets and codes: [Topic-Influence] [Influence-over-Heterogeneous-Networks] [Social-Action-Tracking]
Tutorials are given at WWW'14, WSDM'13 and ASONAM'12, and can be downloaded here [Slides] [PDF].
A survey of models and algorithms for social influence analysis can be found here.