Community detection in complex networks using density-based clustering algorithm and manifold learning

Tao You, Hui Min Cheng, Yi Zi Ning, Ben Chang Shia, Zhong Yuan Zhang

研究成果: 雜誌貢獻文章同行評審

36 引文 斯高帕斯(Scopus)

摘要

Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot be directly applied to community detection due to its inability to recognize the community centers in the network. To solve the problem, a new community detection method (named IsoFdp) is proposed in this paper. First, we use IsoMap technique to map the network data into a low dimensional manifold which can reveal diverse pair-wised similarity. Then Fdp is applied to detect the communities in the network. An improved partition density function is proposed to select the proper number of communities automatically. We test our method on both synthetic and real-world networks, and the results demonstrate the effectiveness of our algorithm over the state-of-the-art methods.
原文英語
頁(從 - 到)221-230
頁數10
期刊Physica A: Statistical Mechanics and its Applications
464
DOIs
出版狀態已發佈 - 十二月 15 2016

ASJC Scopus subject areas

  • 統計與概率
  • 凝聚態物理學

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