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

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)221-230
Number of pages10
JournalPhysica A: Statistical Mechanics and its Applications
Publication statusPublished - Dec 15 2016


  • Community detection
  • Complex network
  • Density-based clustering
  • IsoMap
  • Manifold learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Condensed Matter Physics


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