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Link Prediction via Community Detection in Bipartite Multi-Layer Graphs

Maksim Koptelov 1 Albrecht Zimmermann 1 Bruno Crémilleux 1 Lina F. Soualmia 2
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
2 TIBS - LITIS - Equipe Traitement de l'information en Biologie Santé
LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Abstract : The growing number of multi-relational networks pose new challenges concerning the development of methods for solving classical graph problems in a multi-layer framework, such as link prediction. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in multi-layer graphs. To this end, we extend existing community detection-based link prediction measures to the bipartite multi-layer network setting. We obtain a new generic framework for link prediction in bipartite multi-layer graphs, which can integrate any community detection approach, is capable of handling an arbitrary number of networks, rather inexpensive (depending on the community detection technique), and able to automatically tune its parameters. We test our framework using two of the most common community detection methods, the Louvain algorithm and spectral partitioning, which can be easily applied to bipartite multi-layer graphs. We evaluate our approach on benchmark data sets for solving a common drug-target interaction prediction task in computational drug design and demonstrate experimentally that our approach is competitive with the state-of-the-art.
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Contributor : Bruno Cremilleux <>
Submitted on : Tuesday, February 11, 2020 - 11:45:50 AM
Last modification on : Tuesday, March 3, 2020 - 3:44:03 PM


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Maksim Koptelov, Albrecht Zimmermann, Bruno Crémilleux, Lina F. Soualmia. Link Prediction via Community Detection in Bipartite Multi-Layer Graphs. 35th ACM/SIGAPP Symposium On Applied Computing, Mar 2020, Brno, Czech Republic. ⟨10.1145/3341105.3373874⟩. ⟨hal-02474290⟩



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