2014/07/31 by Giulia Menichetti, Daniel Remondini, Ginestra Bianconi · 33 citations
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Artificial intelligence #Bioinformatics #Bioinformatics and Genomic Networks #Biology #Complex Network Analysis Techniques #Computer science #Data mining #ENCODE #Gene #Gene Regulatory Network Analysis #Genetics #Multiplex #Theoretical computer science #Variety (cybernetics) #cond-mat.dis-nn #cond-mat.stat-mech #physics.bio-ph #physics.soc-ph
paper · pdf · doi:10.1103/physreve.90.062817
published in Physical Review E 90(6), 062817 (American Physical Society)
arxiv created 2014/12/17 · openalex publication_date 2014/12/31 · arxiv updated 2015/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Multiplex networks describe a large number of systems ranging from social networks to the brain. These multilayer structure encode information in their structure. This information can be extracted by measuring the correlations present in the multiplex networks structure, such as the overlap of the links in different layers. Many multiplex networks are also weighted, and the weights of the links can be strongly correlated with the structural properties of the multiplex network. For example, in multiplex network formed by the citation and collaboration networks between PRE scientists it was found that the statistical properties of citations to coauthors differ from the one of citations to noncoauthors, i.e., the weights depend on the overlap of the links. Here we present a theoretical framework for modeling multiplex weighted networks with different types of correlations between weights and overlap. To this end, we use the framework of canonical network ensembles, and the recently introduced concept of multilinks, showing that null models of a large variety of network structures can be constructed in this way. In order to provide a concrete example of how this framework apply to real data we consider a multiplex constructed from gene expression data of healthy and cancer tissues.