2010 SocialActionTrackingviaNoiseTol

• (Tan et al., 2010) ⇒ Chenhao Tan, Jie Tang, Jimeng Sun, Quan Lin, and Fengjiao Wang. (2010). “Social Action Tracking via Noise Tolerant Time-varying Factor Graphs.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835936

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Cited By

• http://scholar.google.com/scholar?q=%22Social+action+tracking+via+noise+tolerant+time-varying+factor+graphs%22+2010
• http://portal.acm.org/citation.cfm?id=1835936&preflayout=flat#citedby

Quotes

Abstract

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 [math]\displaystyle{ t }[/math] is generated by her latent state at [math]\displaystyle{ t }[/math], which is influenced by her attributes, her own latent state at time [math]\displaystyle{ t-1 }[/math] and her neighbors' states at time [math]\displaystyle{ t }[/math] and [math]\displaystyle{ t-1 }[/math]. 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 SocialActionTrackingviaNoiseTol	Jie Tang Jimeng Sun Chenhao Tan Quan Lin Fengjiao Wang			Social Action Tracking via Noise Tolerant Time-varying Factor Graphs		KDD-2010 Proceedings		10.1145/1835804.1835936		2010