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Overlapping community detection in social networks with Voronoi and tolerance neighborhood-based method

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Abstract

Community detection is typically viewed as a graph clustering problem with early detection algorithms focused on detecting non-overlapping communities and formulating various measures and optimization methods to evaluate the quality of clustering. In recent years, overlapping community detection especially in real-world social networks, has become an important and challenging research area since it introduces the possibility of membership of a vertex in more that one community. Overlapping community detection by its definition implies soft clustering and leads to an ideal application of granular computing methods. In this paper, a hybrid computational geometry approach with Voronoi diagrams and tolerance-based neighborhoods (VTNM) is used to detect overlapping communities in social networks. Voronoi partitioning results in a crisp partition of an Euclidean space and a tolerance relation makes it possible to obtain soft partitions. A Voronoi diagram is a method to partition a plane into regions based on nearness to points in a specific set of sites (seeds). In the VTNM approach, these seeds are used as cores for determining tolerance neighborhoods via a non-transitive binary relation. The intersection of these neighborhoods are used to discover overlapping communities. Our proposed VTNM algorithm was tested with 7 small real-world networks and compared with 11 well-known algorithms. VTNM algorithm shows promising results in terms of the Extended Modularity measure, Average F1-score and Normalized Mutual Information (NMI) measure.

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Acknowledgements

This research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) discovery grant # 194376 and is supported by the Queen Elizabeth II Diamond Jubilee scholarship.

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  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, Canada

    Kushagra Trivedi & Sheela Ramanna

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  1. Kushagra Trivedi
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Correspondence to Sheela Ramanna.

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This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant 194376 and Queen Elizabeth II Diamond Jubilee Scholarship.

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Trivedi, K., Ramanna, S. Overlapping community detection in social networks with Voronoi and tolerance neighborhood-based method. Granul. Comput. 6, 95–106 (2021). https://doi.org/10.1007/s41066-019-00207-0

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