1. MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach
- Author
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Pio-Lopez, L��o, Valdeolivas, Alberto, Tichit, Laurent, Remy, ��lisabeth, Baudot, Ana��s, Institut de Mathématiques de Marseille (I2M), Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS), Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Heidelberg University, Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU), Barcelona Supercomputing Center - Centro Nacional de Supercomputacion (BSC - CNS), and Baudot, Anaïs
- Subjects
FOS: Computer and information sciences ,heterogeneous net- work ,Computer Science - Machine Learning ,Molecular Networks (q-bio.MN) ,Science ,network biology ,network embedding ,random walks ,Article ,multiplex network ,Machine Learning (cs.LG) ,Computational biology and bioinformatics ,machine learning ,FOS: Biological sciences ,Medicine ,Quantitative Biology - Molecular Networks ,[INFO]Computer Science [cs] ,[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] ,Data mining ,[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM] ,multi-layer network - Abstract
Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their efficiency for tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several layers containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE method with Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its efficiency. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in the task of link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE., 29 pages, 6 figures
- Published
- 2020
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