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Revisiting neighbourhood proximity based algorithm for overlapping community detection in weighted networks

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Abstract

A network is an indispensable tool for studying complex systems and large data. Any systems or data could be partitioned into components based on traits. This could be interpreted as the community in a network, and detection is essential for comprehending real-world systems and revealing profound insights from diverse realms. Over the past decade, much attention has been given to studying overlapping community structures without considering the weights of connections. However, the current frontier in network science involves expanding this understanding to encompass overlapping community structures with weighted connections. This introduces a new layer of complexity due to the nuanced nature of edge weights, making it an intriguing challenge for modern network science. In this paper, we proposed a refined version of the existing neighbourhood Proximity based Community Detection algorithm and call it ”neighbourhood Proximity based Community Detection using Weighted Centrality (NPCD-WC)” algorithm for extracting overlapping community structures in undirected weighted networks. This proposed algorithm is based on the weighted node centrality concept which makes it more stable and includes improved neighbourhood proximity measures. Subsequently, the proposed algorithm is based on expansion where a chosen node into a community is facilitated through an innovative adaptation of neighbourhood proximity. Two input threshold parameters \(\rho _0, \theta _0\) are needed to output cover of membership communities. Rigorous experimentation on real-world and artificial networks, employing well-established quality-based metrics compelling evidence that NPCD-WC outperforms other baseline algorithms. The algorithm evidenced time complexity better than most baseline algorithms and was highly used for applicable world.

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No datasets were generated or analysed during the current study.

Notes

  1. https://www.santofortunato.net/resources.

  2. https://snap.stanford.edu/data/, https://networkrepository.com/.

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Acknowledgements

Abhinav Kumar would like to thank the Council of Scientific & Industrial Research (CSIR), Government of India for awarding him Senior Research Fellowship [File Number: 09/0466(12390)/2021-EMR-I]. This work is supported by the Science & Engineering Research Board (SERB), Government of India [File Number: EEQ/2023/000980].

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Authors and Affiliations

  1. Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi, Delhi, 110025, India

    Abhinav Kumar & Ravins Dohare

  2. School of Sciences, Indira Gandhi National Open University, Maidan Garhi, New Delhi, Delhi, 110068, India

    Pawan Kumar

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Contributions

Abhinav Kumar: Conceptualization, Methodology, Software, Formal analysis, Visualization, Data curation, Writing—Original Draft, Writing—Review & Editing. Pawan Kumar: Methodology, Software, Formal analysis, Data curation, Writing—Original Draft, Writing—Review & Editing, Supervision, Project Administration. Ravins Dohare: Methodology, Software, Formal analysis, Data curation, Writing—Original Draft, Writing—Review & Editing, Supervision, Project Administration. All authors read and approved the final manuscript.

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Correspondence to Pawan Kumar or Ravins Dohare.

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Kumar, A., Kumar, P. & Dohare, R. Revisiting neighbourhood proximity based algorithm for overlapping community detection in weighted networks. Soc. Netw. Anal. Min. 14, 105 (2024). https://doi.org/10.1007/s13278-024-01257-2

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