Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for analysis of dynamic social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Community detection in large-scale social networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Discovering Temporal Communities from Social Network Documents
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining the core member of terrorist crime group based on social network analysis
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
VCCM mining: mining virtual community core members based on gene expression programming
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
CUT: community update and tracking in dynamic social networks
Proceedings of the 7th Workshop on Social Network Mining and Analysis
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Social network analysis has been a hot topic in the field of graph mining. After people have achieved the goal of detecting communities from various networks, now they are interested in how these explored communities change as time passes by. In other words, people focus on the problem of community evolution and further discover those dynamic characteristics of kinds of networks. Here, we propose CommTracker, a novel and parameter-free algorithm of tracking community evolution, which utilizes the representative quality of core nodes in a community to establish the evolving relationship between two communities in consecutive time snapshots. With such a distinct strategy, it is suitable for analyzing large scale datasets. Depending on relationships established from CommTracker, it is feasible to identify communitysplitand mergence. In addition, one relationship amongst evolution traces, evolutiontracesintersection, is also studied. At last, we demonstrate the correctness and effectiveness of our algorithm on 4 real datasets.