Community detection in complex networks by density-based clustering

Hong Jin, Shuliang Wang and Chenyang Li

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 19, 4606-4618

Abstract: We proposed a method to find the community structure in a complex network by density-based clustering. Physical topological distance is introduced in density-based clustering for determining a distance function of specific influence functions. According to the distribution of the data, the community structures are uncovered. The method keeps a better connection mode of the community structure than the existing algorithms in terms of modularity, which can be viewed as a basic characteristic of community detection in the future. Moreover, experimental results indicate that the proposed method is efficient and effective to be used for community detection of medium and large networks.

Keywords: Physical topological distance; Density-based clustering; Community detection (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:19:p:4606-4618

DOI: 10.1016/j.physa.2013.05.039

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