CommTracker: A Core-Based Algorithm of Tracking Community Evolution

• Published in: Advanced Data Mining and Applications Vol. 5139; pp. 229 - 240
• Main Authors: Wang, Yi, Wu, Bin, Pei, Xin
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2008; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Community Detection; Community Evolution; Core Node; Graph Mining; Social Network Analysis
• ISBN: 3540881913; 9783540881919
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-88192-6_22

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 communitysplit and 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.