TY - GEN
T1 - Predicting aggregate social activities using continuous-time stochastic process
AU - Huang, Shu
AU - Chen, Min
AU - Luo, Bo
AU - Lee, Dongwon
PY - 2012
Y1 - 2012
N2 - How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.
AB - How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.
UR - http://www.scopus.com/inward/record.url?scp=84871102941&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871102941&partnerID=8YFLogxK
U2 - 10.1145/2396761.2396885
DO - 10.1145/2396761.2396885
M3 - Conference contribution
AN - SCOPUS:84871102941
SN - 9781450311564
T3 - ACM International Conference Proceeding Series
SP - 982
EP - 991
BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Y2 - 29 October 2012 through 2 November 2012
ER -