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Detecting semantic‐based communities in node‐attributed graphs
Author(s) -
Sun Heli,
Du Hongxia,
Huang Jianbin,
Sun Zhongbin,
He Liang,
Jia Xiaolin,
Zhao Zhongmeng
Publication year - 2018
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12178
Subject(s) - epigraph , node (physics) , computer science , metric (unit) , process (computing) , semantic similarity , theoretical computer science , artificial intelligence , data mining , mathematics , mathematical optimization , operations management , structural engineering , engineering , economics , operating system
Abstract In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node‐attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each node's perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real‐world data sets show the efficiency and effectiveness of our approach against other state‐of‐the‐art methods.