KEA: practical automatic keyphrase extraction
Proceedings of the fourth ACM conference on Digital libraries
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Detecting Communities in Large Networks by Iterative Local Expansion
CASON '09 Proceedings of the 2009 International Conference on Computational Aspects of Social Networks
An event-based framework for characterizing the evolutionary behavior of interaction graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
A particle-and-density based evolutionary clustering method for dynamic networks
Proceedings of the VLDB Endowment
Community evolution detection in dynamic heterogeneous information networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Tracking the Evolution of Communities in Dynamic Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Finding Communities in Dynamic Social Networks
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Social networks are usually drawn from the interactions between individuals, and therefore are temporal and dynamic in essence. Examining how the structure of these networks changes over time provides insights into their evolution patterns, factors that trigger the changes, and ultimately predict the future structure of these networks. One of the key structural characteristics of networks is their community structure --groups of densely interconnected nodes. Communities in a dynamic social network span over periods of time and are affected by changes in the underlying population, i.e. they have fluctuating members and can grow and shrink over time. In this paper, we introduce a new incremental community mining approach, in which communities in the current time are obtained based on the communities from the past time frame. Compared to previous independent approaches, this incremental approach is more effective at detecting stable communities over time. Extensive experimental studies on real datasets, demonstrate the applicability, effectiveness, and soundness of our proposed framework.