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Community Detection by Affinity Propagation
| Citations: | 1 - 0 self |
BibTeX
@MISC{Liu_communitydetection,
author = {Zhiyuan Liu and Peng Li and Yabin Zheng and Maosong Sun},
title = {Community Detection by Affinity Propagation},
year = {}
}
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Abstract
Abstract. Community structure in networks indicates groups of vertices within which are dense connections and between which are sparse connections. Community detection, an important topic in data mining and social network analysis, has attracted considerable research interests in recent years. Motivated by the idea that community detection is in fact a clustering problem on graphs, we propose several similarity metrics of vertex to transform a community detection problem into a clustering problem, and further adopt a recently-proposed clustering method, namely ‘Affinity Propagation’, to extract communities from graphs. We demonstrate that the method achieves significant quality in detecting community structures in both computer-generated and real-world network data in near-linear time. Furthermore, the method could automatically determine the number of communities. Key words: community detection, vertex similarity, affinity propagation, social network, clustering 1
Keyphrases
community detection affinity propagation clustering problem community structure considerable research interest significant quality key word social network analysis recent year network indicates group social network several similarity metric important topic recently-proposed clustering method real-world network data dense connection data mining community detection problem sparse connection near-linear time vertex similarity
