The Structure of Complex Networks: Theory and Applications

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The Structure of Complex Networks: Theory and Applications

Format:  Hardcover,

465 pages

Publisher: Oxford Univ Pr

Publish Date: Dec 2011

ISBN-13: 9780199591756

ISBN-10: 019959175X

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Book Information

The following content was provided by the publisher.


Publisher: Oxford Univ Pr
Publish Date: Dec 2011
ISBN-13: 9780199591756
ISBN-10: 019959175X
Format: Hardcover
Number of Pages: 465
Shipping Weight (in pounds): 2.6
Product in Inches (L x W x H): 7.5 x 1.1 x 9.7
Walmart No.: 9780199591756

Chapter outline

Theoryp. 1
Introductionp. 3
What are networks?p. 3
When did the story begin?p. 4
A formal definitionp. 6
Why do we study their structure?p. 9
How can we speak their 'language'?p. 14
Adjacency relations in networksp. 25
Node adjacency relationshipp. 25
Degree distributionsp. 27
Degree-degree correlationsp. 31
Link adjacency and beyondp. 36
Discrete Laplacian operatorp. 37
Spectral properties of networksp. 39
Metric and topological structure of networksp. 47
Shortest path distancep. 47
Resistance distancep. 50
Generalized network distancesp. 55
Topological structure of networksp. 57
Fragments (subgraphs) in complex networksp. 73
Network 'graphlets'p. 74
Network motifsp. 76
Closed walks and subgraphsp. 78
Counting small undirected subgraphs analyticallyp. 79
Fragment ratiosp. 82
Accounting for all parts (subgraphs)p. 89
The Estrada indexp. 90
Parameterised Estrada indexp. 95
Network entropy and free energyp. 97
Network returnabilityp. 103
A brief on matrix functionsp. 106
Communicabilify functions in networksp. 109
Network communicabilityp. 109
Communicability in directed and weighted networksp. 113
Generalised communicability functionsp. 114
Communicability based on network vibrationsp. 115
Centralify measuresp. 121
Degree centralityp. 121
Centrality beyond nearest-neighboursp. 125
Closeness centralityp. 140
Betweenness centralityp. 142
Information centralityp. 146
Global network invariantsp. 149
Adjacency-based invariantsp. 149
Distance-based invariantsp. 159
Clumpiness of central nodesp. 161
Self-similarity of complex networksp. 166
Expansion and network classesp. 171
Network expansionp. 171
Universal topological classes of networkp. 173
Topological classification of real-world networksp. 181
Community structure of networksp. 187
Network partition methodsp. 190
Methods based on link centralityp. 198
Methods based on modularityp. 203
Clustering based on similarityp. 206
Communities based on communicabilityp. 209
Network bipartivityp. 215
Bipartivity measuresp. 216
How do we find bipartitions in networks?p. 220
Bipartitions in undirected networksp. 224
Bipartitions in directed networksp. 227
Random models of networksp. 233
'Classical' random networksp. 233
Small-world random networksp. 238
'Scale-free' networksp. 243
Random geometric networksp. 250
Other random modelsp. 252
Applicationsp. 257
Genetic networksp. 259
A primer on gene networksp. 259
Topological properties of gene coexpression networksp. 263
Topological properties of gene regulatory networksp. 267
Other gene networksp. 274
Protein residue networksp. 277
A primer on protein structurep. 277
Protein residue networksp. 280
Small-worldness of residue networksp. 282
Degree distributions of residue networksp. 285
Centrality in residue networksp. 287
Global topological propertiesp. 295
Protein-protein interaction networksp. 301
A primer on protein-protein interactionsp. 301
Global structure of PPI networksp. 304
Subgraph-based techniques in PPI networksp. 311
Bipartite structures in PPI networksp. 316
Local structure of PPI networksp. 319
The structure of reaction networksp. 325
Organic chemical reaction networksp. 326
Reaction networks in the atmospheres of astronomical bodiesp. 330
Metabolic networksp. 333
Anatomical networksp. 345
Intercellular networksp. 345
Vascular networksp. 353
Brain networksp. 355
Ecological networksp. 373
Global topological propertiesp. 374
Searching for keystone speciesp. 383
Landscape networksp. 389
Other network-based ecological studiesp. 395
Social and economic networksp. 399
Brief on socioeconomic network datap. 400
Global topology of social networksp. 401
Social network communitiesp. 408
Centrality in social networksp. 412
Economic networksp. 415
Appendixp. 423
Brain networksp. 423
Ecological networksp. 423
Informational networksp. 424
PPI networksp. 424
Protein residue networksp. 425
Social and economic networksp. 425
Technological networksp. 426
Transcription networksp. 426
Softwarep. 425
Referencesp. 429
Indexp. 457

Book description

This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 chapters are dedicated to the development of theoretical tools for the structural analysis of networks, and 7 chapters are illustrating, in a critical way, applications of these tools to real-world scenarios. The first chapters provide detailed coverage of adjacency and metric and topological properties of networks, followed by chapters devoted to the analysis of individual fragments and fragment-based global invariants in complex networks.

Chapters that analyse the concepts of communicability, centrality, bipartivity, expansibility and communities in networks follow. The second part of this book is devoted to the analysis of genetic, protein residue, protein-protein interaction, intercellular, ecological and socio-economic networks, including important breakthroughs as well as examples of the misuse of structural concepts.

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