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.
Arvustused
Taking into account the self-evident importance of the topic area, time spent reading this book is unlikely to be wasted * Rob Ashmore, Mathematics today * This book provides a summary of the research done during one of the largest and most multidisciplinary projects in network science and complex systems * Mathematical Review Clippings *
| Preface |
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1.1 Multilayer, multiplex, and interconnected networks |
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1.2 Mathematical description of multilayer networks |
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1.3 Random walks in multiplex networks |
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1.4 Centrality and versatility |
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19 | (12) |
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2 Reconstructing Random Jigsaws |
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31 | (20) |
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31 | (3) |
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34 | (1) |
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2.3 Proof of the 0-statement |
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2.4 Towards the 1-statement: Reconstructing large neighborhoods |
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2.5 Proof of the 1-statement |
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46 | (3) |
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49 | (2) |
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3 Classifying Networks with dk-Series |
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51 | (23) |
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51 | (2) |
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3.2 dk-Series for single-layer networks |
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3.3 dk-Series in multilayer networks |
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3.4 Discussion and conclusion |
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4 Economic Specialization and the Nested Bipartite Network of City-Firm Relations |
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4.1 The nested structure of links between cities and economic activities |
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76 | (2) |
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4.2 Learning from ecology: Contribution to nestedness and economic well-being |
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82 | (2) |
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5 Multiplex Modeling of Society |
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5.2 A weighted multilayer model |
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5.3 Modeling channel selection and sampling bias |
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5.4 Summary and discussion |
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6 Data Summaries and Representations: Definitions and Practical Use |
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6.2 Data and representations of data |
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6.3 Putting the data representations to concrete use |
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6.4 Conclusions and perspectives |
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7 Multilevel News Networks |
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116 | (4) |
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7.3 Results and discussion |
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136 | (2) |
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8 The Role of Local Interactions in Cities' Global Networking of Multinational Firms: An SIR Model Applied to Partial-Multiplex Directed Networks |
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138 | (10) |
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138 | (1) |
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139 | (2) |
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8.3 The simulation method |
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141 | (1) |
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142 | (3) |
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145 | (3) |
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9 Self-Organization in Multiplex Networks |
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148 | (11) |
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9.1 The multiplex structure and die dynamics of neural networks |
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148 | (5) |
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9.2 Activator-inhibitor dynamics in multiplex networks |
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153 | (6) |
| References |
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| Index |
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Stefano Battiston is SNF Professor at the Department of Banking and Finance of the University of Zurich. He holds a PhD in Statistical Physics from École Normale Supérieure, Paris. His work applies the complex networks approach both to the empirical analysis of economic networks and the modelling of their dynamics. For several years, his main interests have been financial contagion, default cascades, and propagation of financial distress, where he combines the insights from the statistical mechanics of networks with the analysis of economic incentives. He has been involved in many international projects, including Forecasting Financial Crises, the first European project aimed at anticipating structural instabilities in the global financial networks.
Guido Cadarelli studied Statistical Physics and currently works in the field of Complex Networks. He received his undergraduate degree in 1992 in Rome (La Sapienza) and his PhD in 1996 in Trieste (SISSA). After completing postdocs in Manchester and Cambridge he became firstly "Research Assistant" in INFM and secondly "Primo Ricercatore" at ISC-CNR where he still works as visiting researcher. Presently he is Full Professor of Physics at IMT Lucca and a LIMS Fellow. Since 2015, he has been the Vice-President of the Complex Systems Society. Since 2016, he has been on the board of the SNP Division of European Physical Society.
Antonios Garas obtained a PhD in Physics and a Master's degree in Computational Physics from the Aristotle University of Thessaloniki, and is currently a senior researcher at the Chair of Systems Design at ETH Zurich. Having a background in physics with a strong computational training, he has always been interested in pursuing interdisciplinary research. His research combines methods from statistical physics and graph theory, aiming to understand how the properties of a complex system are influenced by the way the systems's components are linked to each other. Using data-driven modeling and state of the art data-mining techniques, he explores applications of his methodology in Economics, Finance, Physics and Sociology.
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