Multiplex and Multilevel NetworksStefano Battiston, Guido Caldarelli, Antonios Garas Oxford University Press, 2018 - 192 pagine The science of networks represented a substantial change in the way we see natural and technological phenomena. Now we have a better understanding that networks are, in most cases, networks of networks or multi-layered networks. This book provides a summary of the research done during one of the largest and most multidisciplinary projects in network science and complex systems (Multiplex). The science of complex networks originated from the empirical evidence that most of the structures of systems such as the internet, sets of protein interactions, and collaboration between people, share (at least qualitatively) common structural properties. This book examines how properties of networks that interact with other networks can change dramatically. The authors show that, dependent on the properties of links that interconnect two or more networks, we may derive different conclusions about the function and the possible vulnerabilities of the overall system of networks. This book presents a series of novel theoretical results together with their applications, providing a comprehensive overview of the field. |
Sommario
Multilayer Networks | 1 |
Reconstructing Random Jigsaws | 31 |
Classifying Networks with dkSeries | 51 |
Economic Specialization and the Nested Bipartite Network of CityFirm Relations | 74 |
Multiplex Modeling of Society | 84 |
Data Summaries and Representations Definitions and Practical Use | 101 |
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Multiplex and Multilevel Networks Stefano Battiston,Guido Caldarelli,Antonios Garas Anteprima limitata - 2018 |
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1-statement adjacency matrix adjacency tensor aggregated network analysis average degree bipartite network calculate chemical synapses cities clustering coefficient co-occurrence communication complex networks connected component considered contact durations contact matrices context corresponding datasets defined degree correlations degree distribution denote described dk-distributions dk-random graphs dk-series dynamics economic activities edges eigenvalue eigenvector centrality entities equation everyday Figure Financial & Insurance firms follows geographic given global Granovetterian grid indicates individuals infected interactions interlayer connections INTERNET intra-urban intralayer jigsaw k-coreness measure monoplex networks multiplex networks neighbors nestedness nodes nodes of degree null models overlap PageRank pairs of nodes probability properties q)-jigsaw quasiblocks random graphs random walk real network reconstructible relevant represent representations sampling shown single-layer networks social networks structure subgraphs subset surrogate networks temporal networks tensor tiles topology v-template values vector vertex vertices walker weights WSN model