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A subgraphs-density based overlapping community detection algorithm for large-scale complex networks

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Abstract

Overlapping community detection with low computation is one of the fundamental issues and challenges in large-scale complex network analysis. Detecting a community in a network means discovering a cluster of network nodes so that the density of edges between them is high. The existing methods use entire structure information or subgraphs with a fixed size to detect dense communities. Therefore, they are not efficient and accurate for large-scale networks. In this paper, the authors introduce an overlapping community detection algorithm that gradually improves density estimation by expanding the size of subgraphs and gathering information during the search process for finding communities. It is an efficient algorithm with low computational complexity for complex networks with one hundred thousand to several millions of nodes. Experimental results on synthetic and real-world networks with several hundred to four million nodes validate the performance assessment of the proposed method.

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Data Availability

The datasets analyses during the current study are available in the references [50].

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Authors and Affiliations

  1. CSE and IT Department, Shiraz University, Shiraz, Fars, Iran

    Seyed Mojtaba Saif & Ali Hamzeh

  2. Department of Computer Engineering and IT, Jahrom University, Jahrom, Fars, Iran

    Mohammad Ebrahim Samie

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  1. Seyed Mojtaba Saif
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Saif, S.M., Samie, M.E. & Hamzeh, A. A subgraphs-density based overlapping community detection algorithm for large-scale complex networks. Computing 105, 151–185 (2023). https://doi.org/10.1007/s00607-022-01121-1

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