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Distributed Community Prediction for Social Graphs Based on Louvain Algorithm

Abstract : Nowadays, the problem of community detection has become more and more challenging. With application in a wide range of fields such as sociology, digital marketing, bio-informatics, chemical engineering and computer science, the need for scalable and efficient solutions is strongly underlined. Especially, in the rapidly developed and widespread area of social media where the size of the corresponding networks exceeds the hundreds of millions of vertices in the average case. However, the standard sequential algorithms applications have practically proven not only infeasible but also terribly unscalable due to the excessive computation demands and the overdone resources prerequisites. Therefore, the introduction of compatible distributed machine learning solutions seems the most promising option to tackle this NP-hard class problem. The purpose of this work is to propose a novel distributed community detection methodology, based on the supervised community prediction concept that is extremely scalable, remarkably efficient and circumvent the intrinsic adversities of classic community detection approaches.
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Submitted on : Thursday, October 24, 2019 - 12:51:31 PM
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Christos Makris, Dionisios Pettas, Georgios Pispirigos. Distributed Community Prediction for Social Graphs Based on Louvain Algorithm. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.500-511, ⟨10.1007/978-3-030-19823-7_42⟩. ⟨hal-02331330⟩

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