| Description |
1 online resource (185 pages) |
| Contents |
Cover; MULTIPLEX AND MULTILEVEL NETWORKS; Dedication; Copyright; Preface; Contents; Chapter 1: Multilayer Networks; 1.1 Multilayer, multiplex, and interconnected networks; 1.2 Mathematical description of multilayer networks; 1.2.1 Tensorial formalism; 1.2.2 Tensorial nature of adjacency tensors; 1.2.3 Eigenvalue problem with tensors; 1.3 Random walks in multiplex networks; 1.3.1 Navigation on a multiplex network; 1.3.2 Classical random walks; 1.3.3 Diffusive random walks; 1.3.4 Physical random walks; 1.3.5 Maximal entropy random walks; 1.3.6 Comparison of types of random walkers |
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1.3.7 Occupation probability of random walkers1.3.8 Random walks coverage; 1.4 Centrality and versatility; 1.4.1 Eigenvector centrality; 1.4.2 Katz centrality; 1.4.3 Hubs and authorities centrality; 1.4.4 Random walk centralities; 1.4.5 PageRank centrality; 1.4.6 Centrality measures based on shortest path; 1.4.7 Centrality becomes versatility; ACKNOWLEDGMENTS; Chapter 2: Reconstructing Random Jigsaws; 2.1 Introduction; 2.2 Preliminaries; 2.3 Proof of the 0-statement; 2.4 Towards the 1-statement: Reconstructing large neighborhoods; 2.4.1 Constraint graphs; 2.4.2 Windows; 2.4.3 Templates |
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2.4.4 Large templates2.4.5 Small templates; 2.4.6 Proof of the main lemma; 2.5 Proof of the 1-statement; 2.6 Conclusion; ACKNOWLEDGMENTS; Chapter 3: Classifying Networks with dk-Series; 3.1 Introduction; 3.2 dk-Series for single-layer networks; 3.2.1 Classifying single-layer networks based on theirdk-randomness; 3.3 dk-Series in multilayer networks; 3.3.1 Multilayer networks; 3.3.2 Multiplex networks; 3.3.3 Application to real networks; 3.4 Discussion and conclusion; Chapter 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 activities4.2 Learning from ecology: Contribution to nestedness and economic well-being; 4.3 Conclusion; ACKNOWLEDGMENTS; Chapter 5: Multiplex Modeling of Society; 5.1 Introduction; 5.1.1 Stylized facts for social networks; 5.2 A weighted multilayer model; 5.2.1 An uncorrelated multilayer WSN model; 5.2.2 A geographic multilayer WSN model; 5.3 Modeling channel selection and sampling bias; 5.4 Summary and discussion; ACKNOWLEDGMENTS; Chapter 6: Data Summaries and Representations: Definitions and Practical Use; 6.1 Introduction |
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6.2 Data and representations of data6.2.1 Datasets, common summaries, and representations; 6.2.1.1 Statistics; 6.2.1.2 Aggregated networks; 6.2.1.3 Contact matrices; 6.2.2 Novel data representations; 6.2.2.1 Higher-order aggregated networks; 6.2.2.2 Contact matrices of distributions; 6.2.3 Detecting mesoscopic structures; 6.3 Putting the data representations to concrete use; 6.3.1 Comparing datasets; 6.3.2 Using detailed data in data-driven simulations; 6.3.2.1 Using data representations to complement incomplete data; 6.3.2.2 Mesoscale interventions; 6.4 Conclusions and perspectives |
| Summary |
The science of networks represented a substantial change in the way we see natural and technological phenomena. Now we understand that networks are in most of the cases networks of networks or multi-layered networks |
| Notes |
Chapter 7: Multilevel News Networks |
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Print version record |
| Subject |
Computer networks.
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Computer Communication Networks
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Computer networks
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| Form |
Electronic book
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| Author |
Caldarelli, Guido
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Garas, Antonios
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| ISBN |
9780192537386 |
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0192537385 |
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9780191847073 |
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0191847070 |
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