2018/03/05 by Marc Tarrés-Deulofeu, Marc Tarres-Deulofeu, Antonia Godoy-Lorite +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · Psychology · #Artificial intelligence #Bioinformatics and Genomic Networks #Bipartite graph #Block (permutation group theory) #Complex Network Analysis Techniques #Computer network #Computer science #Data mining #Distributed computing #Layer (electronics) #Link (geometry) #Machine learning #Mathematics #Mental Health Research Topics #Node (physics) #Predictive modelling #Scalability #Set (abstract data type) #Stochastic block model #Theoretical computer science #cs.SI #physics.data-an #physics.soc-ph #q-bio.MN #stat.ML
paper · pdf · doi:10.1103/physreve.99.032307
published as Phys. Rev. E 99, 032307 (2019)
arxiv created 2018/03/05 · openalex publication_date 2019/03/27 · arxiv updated 2019/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Many real-world complex systems are well represented as multilayer networks; predicting interactions in those systems is one of the most pressing problems in predictive network science. To address this challenge, we introduce two stochastic block models for multilayer and temporal networks; one of them uses nodes as its fundamental unit, whereas the other focuses on links. We also develop scalable algorithms for inferring the parameters of these models. Because our models describe all layers simultaneously, our approach takes full advantage of the information contained in the whole network when making predictions about any particular layer. We illustrate the potential of our approach by analyzing two empirical data sets: a temporal network of e-mail communications, and a network of drug interactions for treating different cancer types. We find that multilayer models consistently outperform their single-layer counterparts, but that the most predictive model depends on the data set under consideration; whereas the node-based model is more appropriate for predicting drug interactions, the link-based model is more appropriate for predicting e-mail communication.