Abstract
There are an extensive number of algorithms for detecting communities in networks. Modularity maximization technics are the most popular community detection methods. Despite the fact that the modularity measure is the best indicator of the partition quality, it has been proved that such technics suffer from many drawbacks: systematically merging small groups to form larger ones, the tendency to split large and dense groups and the partition of random networks where no community structures exist. In this paper we propose core expansion, a new community detection method that allows to detect communities independently from modularity. The number of communities and their members are discovered without computing the modularity score. We automatically detect the core of each possible community in the network. Then, we iteratively expand each core by adding the nodes to form the final communities. The expansion process is based on the neighborhood overlap measure. Experiments performed on real existing networks proved the performance of our algorithm: Large and dense groups are no more split and almost no communities are discovered in random networks.
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Ali Awada: Deceased March 21, 2019.
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Choumane, A., Awada, A. & Harkous, A. Core expansion: a new community detection algorithm based on neighborhood overlap. Soc. Netw. Anal. Min. 10, 30 (2020). https://doi.org/10.1007/s13278-020-00647-6
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DOI: https://doi.org/10.1007/s13278-020-00647-6