Conversation retrieval for microblogging sites
Information Retrieval
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
SeqiBloc: mining multi-time spanning blockmodels in dynamic graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum margin clustering on evolutionary data
Proceedings of the 21st ACM international conference on Information and knowledge management
Online community detection in social sensing
Proceedings of the sixth ACM international conference on Web search and data mining
Dynamic stochastic blockmodels: statistical models for time-evolving networks
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Community evolution detection in time-evolving information networks
Proceedings of the Joint EDBT/ICDT 2013 Workshops
JobMiner: a real-time system for mining job-related patterns from social media
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Spectral embedding for dynamic social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Proceedings of the 17th International Database Engineering & Applications Symposium
A modelling framework for social media monitoring
International Journal of Web Engineering and Technology
Adaptive evolutionary clustering
Data Mining and Knowledge Discovery
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Although a large body of work is devoted to finding communities in static social networks, only a few studies examined the dynamics of communities in evolving social networks. In this paper, we propose a dynamic stochastic block model for finding communities and their evolution in a dynamic social network. The proposed model captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network. Unlike many existing approaches for modeling social networks that estimate parameters by their most likely values (i.e., point estimation), in this study, we employ a Bayesian treatment for parameter estimation that computes the posterior distributions for all the unknown parameters. This Bayesian treatment allows us to capture the uncertainty in parameter values and therefore is more robust to data noise than point estimation. In addition, an efficient algorithm is developed for Bayesian inference to handle large sparse social networks. Extensive experimental studies based on both synthetic data and real-life data demonstrate that our model achieves higher accuracy and reveals more insights in the data than several state-of-the-art algorithms.