The Structure of Complex Networks Theory and Applications 1st Edition by Ernesto Estrada ISBN 019959175X 9780199591756
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ISBN 10: 019959175X
ISBN 13: 9780199591756
Author: Ernesto Estrada
The Structure of Complex Networks Theory and Applications 1st Table of contents:
Part I: Theory
Chapter 1: Introduction
1.1 What are networks?
1.2 When did the story begin?
1.3 A formal definition
1.4 Why do we study their structure?
1.5 How can we speak their ‘language’?
Chapter 2: Adjacency Relations in Networks
2.1 Node adjacency relationship
2.2 Degree distributions
2.3 Degree–degree correlations
2.4 Link adjacency and beyond
2.5 Discrete Laplacian operator
2.6 Spectral properties of networks
Chapter 3: Metric and Topological Structure of Networks
3.1 Shortest path distance
3.2 Resistance distance
3.3 Generalized network distances
3.4 Topological structure of networks
Chapter 4: Fragments (Subgraphs) in Complex Networks
4.1 Network ‘graphlets’
4.2 Network motifs
4.3 Closed walks and subgraphs
4.4 Counting small undirected subgraphs analytically
4.5 Fragment ratios
Chapter 5: Accounting for All Parts (Subgraphs)
5.1 The Estrada index
5.2 Parameterised Estrada index
5.3 Network entropy and free energy
5.4 Network returnability
5.5 A brief on matrix functions
Chapter 6: Communicability Functions in Networks
6.1 Network communicability
6.2 Communicability in directed and weighted networks
6.3 Generalised communicability functions
6.4 Communicability based on network vibrations
Chapter 7: Centrality Measures
7.1 Degree centrality
7.2 Centrality beyond nearest-neighbours
7.3 Closeness centrality
7.4 Betweenness centrality
7.5 Information centrality
Chapter 8: Global Network Invariants
8.1 Adjacency-based invariants
8.2 Distance-based invariants
8.3 Clumpiness of central nodes
8.4 Self-similarity of complex networks
Chapter 9: Expansion and Network Classes
9.1 Network expansion
9.2 Universal topological classes of networks
9.3 Topological classification of real-world networks
Chapter 10: Community Structure of Networks
10.1 Network partition methods
10.2 Methods based on link centrality
10.3 Methods based on modularity
10.4 Clustering based on similarity
10.5 Communities based on communicability
Chapter 11: Network Bipartivity
11.1 Bipartivity measures
11.2 How do we find bipartitions in networks?
11.3 Bipartitions in undirected networks
11.4 Bipartitions in directed networks
Chapter 12: Random Models of Networks
12.1 ‘Classical’ random networks
12.2 Small-world random networks
12.3 ‘Scale-free’ networks
12.4 Random geometric networks
12.5 Other random models
Part II: Applications
Chapter 13: Genetic Networks
13.1 A primer on gene networks
13.2 Topological properties of gene coexpression networks
13.3 Topological properties of gene regulatory networks
13.4 Other gene networks
Chapter 14: Protein Residue Networks
14.1 A primer on protein structure
14.2 Protein residue networks
14.3 Small-worldness of residue networks
14.4 Degree distributions of residue networks
14.5 Centrality in residue networks
14.6 Global topological properties
Chapter 15: Protein–Protein Interaction Networks
15.1 A primer on protein–protein interactions
15.2 Global structure of PPI networks
15.3 Subgraph-based techniques in PPI networks
15.4 Bipartite structures in PPI networks
15.5 Local structure of PPI networks
Chapter 16: The Structure of Reaction Networks
16.1 Organic chemical reaction networks
16.2 Reaction networks in the atmospheres of astronomical bodies
16.3 Metabolic networks
Chapter 17: Anatomical Networks
17.1 Intercellular networks
17.2 Vascular networks
17.3 Brain networks
Chapter 18: Ecological Networks
18.1 Global topological properties
18.2 Searching for keystone species
18.3 Landscape networks
18.4 Other network-based ecological studies
Chapter 19: Social and Economic Networks
19.1 Brief on socioeconomic network data
19.2 Global topology of social networks
19.3 Social network communities
19.4 Centrality in social networks
19.5 Economic networks
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Tags: Ernesto Estrada, Complex Networks