Probabilistic Multilayer Networks

2018/08/23 by Enrique Hernández–Lemus, Enrique Hernández-Lemus, Hernández-Lemus, Enrique +6
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #Bioinformatics and Genomic Networks #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #cs.SI #physics.data-an #physics.soc-ph

paper · pdf · doi:10.48550/arxiv.1808.07857

9 pages, 2 Figures, 1 Table, 3 Appendices

openalex publication_date 2018/08/23 · arxiv created 2021/07/10 · arxiv updated 2021/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Here we introduce probabilistic weighted and unweighted multilayer networks as derived from information theoretical correlation measures on large multidimensional datasets. We present the fundamentals of the formal application of probabilistic inference on problems embedded in multilayered environments, providing examples taken from the analysis of biological and social systems: cancer genomics and drug-related violence.

Citations

• A tool for filtering information in complex systems
• Layer aggregation and reducibility of multilayer interconnected networks

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