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Determining maximum cliques for community detection in weighted sparse networks

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Abstract

In this article, we have delved into detecting dense regions of weighted sparse networks through identification of cliques and communities. A novel clique finding method is introduced to generate all maximum cliques of a sparse network, with focus on analysis of real-life networks and a community detection algorithm is devised on maximal cliques to determine possible overlapping communities of the weighted sparse network. A good number of methods of clique detection already exist, some of which are truly efficient, but many of them lack direct applicability to real-life network analysis, as they deal with simple networks, hide intermediary details and allow cliques to be formed without information on strength of interactions in group behavior. The proposed method attaches a clique-intensity value with every clique and using two thresholds on intensity of interactions, at the individual level and group level, provides handle to filter stray or insignificant interactions at various stages of clique formation. Using differently sized weighted maximal cliques as building blocks, a new overlapping community detection method is proposed using a new measure, a weighted version of Jaccard index, called weighted Jaccard index. Experiments are done on real-life networks; the maximum clique structures reveal sensitivity to threshold values, while the resulting community structures demonstrate efficacy of the community detection method.

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Acknowledgements

With deep gratitude, we acknowledge the contribution of late Prof C. A. Murthy of the Indian Statistical Institute toward producing this article. He was thoroughly involved with the work contained in this article until he breathed his last. This work has been a part of the project titled Characterization and Analysis of Interaction Networks under the Women Scientist Fellowship scheme supported by the Department of Science and Technology, Government of India.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. The research explores various theoretical aspects on weighted graph, determines maximum cliques from the sparse graph and finally detects overlapping communities from the real-life sparse network models based on our proposed modified weighted Jaccard index similarity. This paper may be an interest to the readers of your journal. We affirm that this manuscript is original, has not been published before. It is not currently being considered for publication elsewhere and has no potential conflict of interest to declare.

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Authors and Affiliations

  1. Advanced Computing and Microelectronics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata, 700108, India

    Swati Goswami

  2. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, West Bengal, 711103, India

    Swati Goswami & Asit Kumar Das

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  1. Swati Goswami
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  2. Asit Kumar Das
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Correspondence to Swati Goswami.

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Goswami, S., Das, A.K. Determining maximum cliques for community detection in weighted sparse networks. Knowl Inf Syst 64, 289–324 (2022). https://doi.org/10.1007/s10115-021-01631-y

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