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RT Book, Whole SR Electronic DC OPAC T1 Multilayer Networks: Analysis and Visualization : Introduction to muxViz with R / by Manlio De Domenico A1 De Domenico, Manlio A1 SpringerLink (Online service) YR 2022 FD 2022 SP XXXI, 105 p K1 Computer networks K1 Telecommunication K1 Business information services K1 Computer science -- Mathematics K1 Mathematical statistics K1 Information visualization K1 System theory K1 Computer Communication Networks K1 Communications Engineering, Networks K1 IT in Business K1 Probability and Statistics in Computer Science K1 Data and Information Visualization K1 Complex Systems ED 1st ed. 2022. PB Springer International Publishing : Imprint: Springer PP Cham SN 9783030757182 LA English (英語) CL LCC:TK5105.5-5105.9 CL DC23:004.6 NO Part 1. Multilayer Network Science: Analysis and Visualization -- 1. Introduction -- 2. Multilayer Networks: Overview -- 3. Multilayer Analysis: Fundamentals and Micro-scale -- 4. Multilayer Versatility and Triads -- 5. Multilayer Organization: Meso-scale -- 6. Other Multilayer Analyses based on Dynamical Processes -- 7. Visualizing Multilayer Networks and Data -- Part 2. Appendices -- A. Installing and Using muxViz NO The adoption of multilayer analysis techniques is rapidly expanding across all areas of knowledge, from social sciences (the first facing the complexity of such structures, decades ago) to computer science, from biology to engineering. However, until now, no book has dealt exclusively with the analysis and visualization of multilayer networks. Multilayer Networks: Analysis and Visualization provides a guided introduction to one of the most complete computational frameworks, named muxViz, with introductory information about the underlying theoretical aspects and a focus on the analytical side. Dozens of analytical scripts and examples to use the muxViz library in practice, by means of the Graphical User Interface or by means of the R scripting language, are provided. In addition to researchers in the field of network science, as well as practitioners interested in network visualization and analysis, this book will appeal to researchers without strong technical or computer science background who want to learn how to use muxViz software, such as researchers from humanities, social science and biology: audiences which are targeted by case studies included in the book. Other interdisciplinary audiences include computer science, physics, neuroscience, genetics, urban transport and engineering, digital humanities, social and computational social science. Readers will learn how to use, in a very practical way (i.e., without focusing on theoretical aspects), the algorithms developed by the community and implemented in the free and open-source software muxViz. The data used in the book is available on a dedicated (open and free) site NO HTTP:URL=https://doi.org/10.1007/978-3-030-75718-2 NO 書誌ID=EB00002049; LK [E Book]https://doi.org/10.1007/978-3-030-75718-2 OL 30