Chenhao Tan, Jie Tang, Jimeng Sun, Quan Lin, Fengjiao Wang
In Proceedings of the Sixteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'2010).
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 discover interesting patterns of the social dynamics. Quantitatively, experimental results show that the proposed method outperforms several baseline methods for social action prediction.
[Slides][Video][PDF] [A companion page with details that I made as an undergrad]
@inproceedings{tan+etal:10,
author = {Chenhao Tan and Jie Tang and Jimeng Sun and Quan Lin and Fengjiao Wang},
title = {Social Action Tracking via Noise Tolerant Time-varying Factor Graphs},
year = {2010},
booktitle = {Proceedings of KDD}
}