IEEE Internet Computing
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
CommTracker: A Core-Based Algorithm of Tracking Community Evolution
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Detecting Structural Changes and Command Hierarchies in Dynamic Social Networks
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
CommTrend: An Applied Framework for Community Detection in Large-Scale Social Network
FSKD '09 Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Detection of Overlapping Communities in Dynamical Social Networks
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Detecting and Tracking Community Dynamics in Evolutionary Networks
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
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Social network exhibits a special property: community structure. The community detection on a social network is like clustering on a graph, but the nodes in social network has unique name and the edges has some special properties like friendship, common interest. There have been many clustering methods can be used to detect the community structure on a static network. But in real-world, the social networks are usually dynamic, and the community structures always change over time. We propose Community Update and Tracking algorithm, CUT, to efficiently update and track the community structure algorithm in dynamic social networks. When the social network has some variations in different timestamps, we track the seeds of community and update the community structure instead of recalculating all nodes and edges in the network. The seeds of community is the base of community, we find some nodes which connected together tightly, and these nodes probably become communities. Therefore, our approach can quickly and efficiently update the community structure.