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Enriching networks with edge insertion to improve community detection

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Abstract

Community detection is a broad area of study in network science, in which its correct detection helps to get information about the groups and the relationships between their nodes. Community detection algorithms use the available snapshot of a network to detect its underlying communities. But, if this snapshot is incomplete, the algorithms may not recover the correct communities. This work proposes a set of link prediction heuristics using different network properties to estimate a more complete version of the network and improve the community detection algorithms. Each heuristic returns the most likely edges to be observed in a future version of the network. We performed experiments on real-world and artificial networks with different insertion sizes, comparing the results with two approaches: (i) without using edge insertion and (ii) using the EdgeBoost algorithm, based on node similarity measures. The experiments show that some of our proposed heuristics improve the results of traditional community detection algorithms. This improvement is even more prominent for networks with poorly defined structures.

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Notes

  1. Source code available at https://github.com/EricCamacho/LINE.

  2. Library with network analysis tools and that can be used in Python, R, and C/C++ languages (Csardi and Nepusz 2006).

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

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Authors and Affiliations

  1. Federal University of ABC (UFABC), Santo André, Brazil

    Éric Tadeu Camacho de Oliveira & Fabrício Olivetti de França

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  1. Éric Tadeu Camacho de Oliveira
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  2. Fabrício Olivetti de França
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Correspondence to Éric Tadeu Camacho de Oliveira.

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de Oliveira, É.T.C., de França, F.O. Enriching networks with edge insertion to improve community detection. Soc. Netw. Anal. Min. 11, 89 (2021). https://doi.org/10.1007/s13278-021-00803-6

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  • DOI: https://doi.org/10.1007/s13278-021-00803-6

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