TY - GEN
T1 - Social action tracking via Noise Tolerant Time-varying Factor Graphs
AU - Tan, Chenhao
AU - Tang, Jie
AU - Sun, Jimeng
AU - Lin, Quan
AU - Wang, Fengjiao
PY - 2010/9/7
Y1 - 2010/9/7
N2 - 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.
AB - 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.
KW - Social action tracking
KW - Social influence analysis
KW - Time-varying Factor Graphs
UR - http://www.scopus.com/inward/record.url?scp=77956224695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956224695&partnerID=8YFLogxK
U2 - 10.1145/1835804.1835936
DO - 10.1145/1835804.1835936
M3 - Conference contribution
AN - SCOPUS:77956224695
SN - 9781450300551
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1049
EP - 1058
BT - KDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
T2 - 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010
Y2 - 25 July 2010 through 28 July 2010
ER -