Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Structural and temporal analysis of the blogosphere through community factorization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Discovering Temporal Communities from Social Network Documents
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Real-world social networks (e.g., blogosphere) often evolve over time and thus poses challenges on conventional social network analysis techniques which model the underlying networks as static graphs. In this paper, we are interested in detecting dynamic communities and their trend of evolution in a social network by examining the structural and dynamic patterns of interactions. In doing so, we propose an iterative mining algorithm for computing the intensities and bursts of some hidden communities over time. Our method is probabilistic in nature and can be applied to both undirected graphs and directed graphs. Quantitative and qualitative performance comparisons between the proposed method and some representative methods for social network analysis are provided. Evaluation results based on three benchmark datasets, including Reuters terror news network, political blogosphere, and Enron emails, show that the proposed method is both effective and efficient.