2018/01/30 by Giuseppe Mangioni, Mangioni, Giuseppe, Giuseppe Jurman +3
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Biological Physics (physics.bio-ph) #Complex Network Analysis Techniques #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Molecular Networks (q-bio.MN) #Physics and Society (physics.soc-ph)
paper · pdf · doi:10.48550/arxiv.1801.10144
openalex publication_date 2018/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A variety of complex systems exhibit different types of relationships\nsimultaneously that can be modeled by multiplex networks. A typical problem is\nto determine the community structure of such systems that, in general, depend\non one or more parameters to be tuned. In this study we propose one measure,\ngrounded on information theory, to find the optimal value of the relax rate\ncharacterizing Multiplex Infomap, the generalization of the Infomap algorithm\nto the realm of multilayer networks. We evaluate our methodology on synthetic\nnetworks, to show that the most representative community structure can be\nreliably identified when the most appropriate relax rate is used. Capitalizing\non these results, we use this measure to identify the most reliable meso-scale\nfunctional organization in the human protein-protein interaction multiplex\nnetwork and compare the observed clusters against a collection of independently\nannotated gene sets from the Molecular Signatures Database (MSigDB). Our\nanalysis reveals that modules obtained with the optimal value of the relax rate\nare biologically significant and, remarkably, with higher functional content\nthan the ones obtained from the aggregate representation of the human proteome.\nOur framework allows us to characterize the meso-scale structure of those\nmultilayer systems whose layers are not explicitly interconnected each other --\nas in the case of edge-colored models -- the ones describing most biological\nnetworks, from proteomes to connectomes.\n