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

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

Physica A: Statistical Mechanics and its Applications, 2016, vol. 464, issue C, 221-230

Abstract: 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.

Keywords: Complex network; Community detection; Density-based clustering; Manifold learning; IsoMap (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:464:y:2016:i:c:p:221-230

DOI: 10.1016/j.physa.2016.07.025

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