Social action tracking via noise tolerant time-varying factor graphs

Users’ behaviors (actions) in a social network are influenced by various factors such as personal interests, social influence, and global trends. However, few publications systematically study how social actions evolve in a dynamic social network and to what extent different factors affect the user actions. In this paper, we propose a Noise Tolerant Time-varying Factor Graph Model (NTT-FGM) for modeling and predicting social actions. NTT-FGM simultaneously models social network structure, user attributes and user action history for better prediction of the users’ future actions. More specifically, a user’s action at time t is generated by her latent state at t, which is influenced by her attributes, her own latent state at time t − 1 and her neighbors’ states

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Chenhao Tan, Jie Tang, Jimeng Sun, Quan Lin, Fengjiao Wang
• Keywords: Data Mining, KDD 2010, Social Actions, Social Network, User Action