The Community for Technology Leaders
2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 53-60
Wenhao Gao , School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
Wenjian Luo , School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
Chenyang Bu , School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, Anhui, China
ABSTRACT
Evolutionary community discovery is a hot research topic which clusters the dynamic or temporal network. The communities detected in dynamic network should get reasonable partition for the current data while simultaneously not deviate drastically from the previous ones. In this paper, the evolutionary community discovery algorithm based on leader nodes (EvoLeaders) is proposed to cluster the dynamic network. Compared with the static community discovery algorithm based on leader nodes (the Top Leaders algorithm), experimental results over two real-world datasets demonstrate that the EvoLeaders is more suitable for dynamic scenarios.
INDEX TERMS
Heuristic algorithms, Clustering algorithms, Current measurement, Partitioning algorithms, Social network services, History, Merging
CITATION

Wenhao Gao, Wenjian Luo and Chenyang Bu, "Evolutionary community discovery in dynamic networks based on leader nodes," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 53-60.
doi:10.1109/BIGCOMP.2016.7425801
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