document: Social action tracking via noise tolerant time-varying factor graphs It is well known that 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 at time t and t-1 . Based on this intuition , we formalize the social action tracking problem using the NTT-FGM model ; then present an efficient algorithm to learn the model , by combining the ideas from both continuous linear system and Markov random field . Finally , we present a case study of our model on predicting future social actions . We validate the model on three different types of real-world data sets . Qualitatively , our model can uncover some interesting patterns of the social dynamics . Quantitatively , experimental results show that the proposed method outperforms several baseline methods for action prediction .
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