title: Weighted multiplex networks.
abstract: One of the most important challenges in network science is to quantify the information encoded in complex network structures. Disentangling randomness from organizational principles is even more demanding when networks have a multiplex nature. Multiplex networks are multilayer systems of [Formula: see text] nodes that can be linked in multiple interacting and co-evolving layers. In these networks, relevant information might not be captured if the single layers were analyzed separately. Here we demonstrate that such partial analysis of layers fails to capture significant correlations between weights and topology of complex multiplex networks. To this end, we study two weighted multiplex co-authorship and citation networks involving the authors included in the American Physical Society. We show that in these networks weights are strongly correlated with multiplex structure, and provide empirical evidence in favor of the advantage of studying weighted measures of multiplex networks, such as multistrength and the inverse multiparticipation ratio. Finally, we introduce a theoretical framework based on the entropy of multiplex ensembles to quantify the information stored in multiplex networks that would remain undetected if the single layers were analyzed in isolation.
authors.affiliation: Department of Physics and Astronomy and INFN Sez. Bologna, Bologna University, Bologna, Italy.
authors.forename: Giulia
authors.initials: G
authors.lastname: Menichetti
authors.affiliation: Department of Physics and Astronomy and INFN Sez. Bologna, Bologna University, Bologna, Italy.
authors.forename: Daniel
authors.initials: D
authors.lastname: Remondini
authors.affiliation: School of Business and Management, Queen Mary University of London, London, United Kingdom.
authors.forename: Pietro
authors.initials: P
authors.lastname: Panzarasa
authors.affiliation: School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom.
authors.forename: Raúl J
authors.initials: RJ
authors.lastname: Mondragón
authors.affiliation: School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom.
authors.forename: Ginestra
authors.initials: G
authors.lastname: Bianconi
mesh_terms: D016571:Neural Networks, Computer