ArticleScientific reports2021
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 1 citations in OpenAlex.
- Engineered Living Systems With Self-Organizing Neural Networks: From Anatomy to Behavior and Gene Expression.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modeling roles and trade-offs in multiplex networks.Nature communications · 2026Article
- Multilayer network approaches to omics data integration in digital twins for cancer research.Frontiers in systems biology · 2026Review
- Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer.BMC bioinformatics · 2025Article
- Basal Xenobot transcriptomics reveals changes and novel control modality in cells freed from organismal influence.Communications biology · 2025Article
- A collaborative network analysis for the interpretation of transcriptomics data in Huntington's disease.Scientific reports · 2025Article
- MiDNE a tool for Multi-omics genes and drugs interactions discovery.Computational and structural biotechnology journal · 2025Article
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.Neural networks : the official journal of the International Neural Network Society · 2025Article
- MoNETA: MultiOmics Network Embedding for SubType Analysis.NAR genomics and bioinformatics · 2024Article
- Inference of Gene Regulatory Networks Based on Multi-view Hierarchical Hypergraphs.Interdisciplinary sciences, computational life sciences · 2024Article
- Motif-based community detection in heterogeneous multilayer networks.Scientific reports · 2024Article
- Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.Nature communications · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Feature-rich multiplex lexical networks reveal mental strategies of early language learning.Scientific reports · 2023Article
- DrDimont: explainable drug response prediction from differential analysis of multi-omics networks.Bioinformatics (Oxford, England) · 2022Article
- PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm.Frontiers in genetics · 2022Article
- Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2022Review
- Recommendation algorithm based on attributed multiplex heterogeneous network.PeerJ. Computer science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in 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 multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using 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 performance. 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 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 .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.