Discovering communities in complex networks by edge label propagation
Author(s) -
Wei Liu,
Xingpeng Jiang,
Matteo Pellegrini,
Xiaofan Wang
Publication year - 2016
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep22470
Subject(s) - computer science , node (physics) , enhanced data rates for gsm evolution , link (geometry) , community structure , complex network , data science , evolving networks , data mining , theoretical computer science , artificial intelligence , world wide web , computer network , biology , ecology , structural engineering , engineering
The discovery of the community structure of real-world networks is still an open problem. Many methods have been proposed to shed light on this problem, and most of these have focused on discovering node community. However, link community is also a powerful framework for discovering overlapping communities. Here we present a novel edge label propagation algorithm (ELPA), which combines the natural advantage of link communities with the efficiency of the label propagation algorithm (LPA). ELPA can discover both link communities and node communities. We evaluated ELPA on both synthetic and real-world networks, and compared it with five state-of-the-art methods. The results demonstrate that ELPA performs competitively with other algorithms.
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