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Fast detection of community structures using graph traversal in social networks

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Abstract

Finding community structures in social networks is considered to be a challenging task as many of the proposed algorithms are computationally expensive and does not scale well for large graphs. Most of the community detection algorithms proposed till date are unsuitable for applications that would require detection of communities in real time, especially for massive networks. The Louvain method, which uses modularity maximization to detect clusters, is usually considered to be one of the fastest community detection algorithms even without any provable bound on its running time. We propose a novel graph traversal-based community detection framework, which not only runs faster than the Louvain method but also generates clusters of better quality for most of the benchmark datasets. We show that our algorithms run in \(O(|V| + |E|)\) time to create an initial cover before using modularity maximization to get the final cover.

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Notes

  1. The code can be downloaded from https://github.com/sna-lincom/LINCOM.

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Acknowledgements

We would like to thank the reviewers for their effort for thoroughly reading our paper and for suggesting valuable changes. We would also like to thank Upasana Dutta for pointing out and correcting errors in some of the figures and algorithms that appear in this work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, WB, India

    Partha Basuchowdhuri, Varsha Nagarajan, Khusbu Mishra, Surabhi Gupta & Subhashis Majumder

  2. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

    Satyaki Sikdar

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  1. Partha Basuchowdhuri
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  2. Satyaki Sikdar
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Corresponding author

Correspondence to Partha Basuchowdhuri.

Additional information

Satyaki Sikdar: The work was done when the author was at Heritage Institute of Technology.

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Basuchowdhuri, P., Sikdar, S., Nagarajan, V. et al. Fast detection of community structures using graph traversal in social networks. Knowl Inf Syst 59, 1–31 (2019). https://doi.org/10.1007/s10115-018-1209-7

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