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Characterizing community detection algorithms and detected modules in large-scale complex networks

Vinh-Loc Dao 1, 2
2 Lab-STICC_IMTA_CID_DECIDE
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Community detection is a technique used to separate graphs into several densely connected groups of vertices, especially powerful when visualization techniques are infeasible for large-scale structures of networks. Thanks to aplethora of potential applications in the golden age of social interaction, many detection techniques have been invented in the last decades. Their performance in discovering significant structures has been a hot topic in the network science community since there is still no consensus on what good communities are. In this dissertation, we invite readers to go through several comprehensive analyses of various state-of-the-art community detection methods as well as modular structures of real networks belonging to a large variety of domains. Our results provide intuitive illustrations of community structures and useful information that helps readers to choose their context-based rule-of-thumb solution.
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Submitted on : Monday, May 6, 2019 - 3:07:08 PM
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  • HAL Id : tel-02121358, version 1

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Vinh-Loc Dao. Characterizing community detection algorithms and detected modules in large-scale complex networks. Data Structures and Algorithms [cs.DS]. Ecole nationale supérieure Mines-Télécom Atlantique, 2018. English. ⟨NNT : 2018IMTA0108⟩. ⟨tel-02121358⟩

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