A Modular Overlapping Community Detection Algorithm: Investigating the ``From Local to Global'' Approach

Abstract : We propose an overlapping community detection algorithm following a “from local to global approach”: our algorithm finds local communities one by one by repetitively optimizing a quality function that measures the quality of a community. Then, as some extracted local communities can be very similar to each-other, a cleaning procedure is applied to obtain the global overlapping community structure. Our algorithm depends on three modules: (i) a quality function, (ii) an optimization heuristic and (iii) a cleaning procedure. Various such modules can be independently plugged in. We show that, using default modules, our algorithm improves over a state-of-the-art method on some real-world graphs with ground truth communities. In the future we would like to study which combination of modules performs best in practice and make our code parallel.
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https://hal.archives-ouvertes.fr/hal-02364496
Contributor : Jean-Loup Guillaume <>
Submitted on : Friday, November 15, 2019 - 8:25:13 AM
Last modification on : Sunday, November 17, 2019 - 1:24:22 AM

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  • HAL Id : hal-02364496, version 1

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Maximilien Danisch, Noe Gaumont, Jean-Loup Guillaume. A Modular Overlapping Community Detection Algorithm: Investigating the ``From Local to Global'' Approach. 16th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, Jun 2018, Paris, France. pp.167-170. ⟨hal-02364496⟩

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