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E-raamat: Large Scale Structure And Dynamics Of Complex Networks: From Information Technology To Finance And Natural Science

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Edited by (Univ Of Indiana, Usa), Edited by (Univ Degli Studi Di Roma La Sapienza, Italy)
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This book is the culmination of three years of research effort on a multidisciplinary project in which physicists, mathematicians, computer scientists and social scientists worked together to arrive at a unifying picture of complex networks. The contributed chapters form a reference for the various problems in data analysis visualization and modeling of complex networks.
Preface v
Introduction 1(4)
Preliminaries and Basic Definitions in Network Theory
5(12)
Introduction
5(1)
Basic definitions
5(2)
Different kinds of graphs
7(2)
Weighted, directed and oriented graphs
7(1)
Subgraphs
8(1)
Partited graphs
9(1)
Paths and cycles
9(2)
Trees
10(1)
Statistics on graphs
11(3)
Small world properties
11(1)
Clustering coefficient
12(1)
Degree distribution
12(2)
Complexity
14(1)
What is next
15(2)
Models of Complex Networks
17(18)
Introduction
17(1)
Random networks
18(5)
Erdos-Renyi networks
18(3)
Fitness networks
21(2)
Preferential attachment networks
23(9)
The Barabasi-Albert model
25(2)
Duplication-divergence models
27(3)
Growing weighted networks
30(2)
The small-world model
32(1)
Outlook
33(2)
Correlations in Complex Networks
35(32)
Introduction
35(1)
Detailed balance condition
36(3)
Empirical measurement of correlations
39(8)
Two vertices correlations: ANND
41(4)
Three vertices correlations: Clustering
45(2)
Networks in the real world
47(7)
Pretty-good-privacy web of trust
51(3)
Modeling correlations
54(11)
Disassortative correlations
55(1)
The configuration model
55(1)
Growing models
56(1)
Assortativity generators
57(2)
Modeling clustered networks
59(1)
Random graphs with attributes
60(1)
Hidden color models
61(1)
Fitness or hidden variables models
62(2)
Fitness and preferential attachment models
64(1)
Outlook
65(2)
The Architecture of Complex Weighted Networks: Measurements and Models
67(26)
Introduction
67(1)
Tools for the characterization of weighted networks
68(4)
Weights
68(1)
Degree and weight distributions
68(1)
Weighted degree: Strength
68(1)
Weighted clustering
69(1)
Weighted assortativity: Affinity
70(1)
Local heterogeneity
71(1)
Weighted networks: Empirical results
72(11)
Transportation networks
73(1)
Airport network
73(4)
Urban and inter-urban movement networks
77(1)
Transportation networks: Summary
78(1)
Social network: Example of the scientific collaboration network
79(4)
Biological network: The case of the metabolic network
83(1)
Modeling weighted networks
83(8)
Coupling weight and topology
83(1)
A simple model: Weight perturbation and ``busy get busier'' effects
84(4)
Local heterogeneities, nonlinearities and space-topology coupling
88(2)
Other models coupling traffic and topology
90(1)
Outlook
91(2)
Community Structure Identification
93(22)
Introduction
93(1)
Definitions of communities
94(2)
Evaluating community identification
96(1)
Link removal methods
97(1)
Shortest path centrality
97(1)
Current-flow and random walk centrality
98(2)
Information centrality
99(1)
Link clustering
100(1)
Agglomerative methods
100(2)
Hierarchical clustering
100(1)
L-shell method
101(1)
Methods based on maximising modularity
102(2)
Greedy algorithm
102(1)
Simulated annealing methods
102(1)
Extremal optimisation
103(1)
Spectral analysis methods
104(3)
Spectral bisection
104(1)
Multi dimensional spectral analysis
105(1)
Constrained optimisation
106(1)
Approximate resistance networks
106(1)
Other methods
107(4)
Clustering and curvature
107(1)
Random walk based methods
108(2)
Q-potts model
110(1)
Comparative evaluation
111(2)
Conclusion
113(2)
Visualizing Large Complex Networks
115(18)
Introduction
115(1)
Global methods for visualizing large graphs
116(8)
Spring embedder based methods
117(1)
Properties
118(3)
Spectral layout
121(1)
Properties
122(2)
Analytical layouts
124(9)
Centrality and status layouts
125(1)
Clustered layouts
126(3)
Case studies
129(4)
Modeling the Webgraph: How Far We Are
133(29)
Introduction
133(1)
Preliminaries
134(3)
WebBase
137(5)
In-degree and out-degree
138(2)
PageRank
140(1)
Bipartite cliques
141(1)
Strongly connected components
142(1)
Stochastic models of the webgraph
142(7)
Models of the webgraph
143(1)
A multi-layer model
144(2)
Large scale simulation
146(3)
Algorithmic techniques for generating and measuring webgraphs
149(12)
Data representation and multifiles
151(1)
Generating webgraphs
152(2)
Traversal with two bits for each node
154(1)
Semi-external breadth first search
154(1)
Semi-external depth first search
155(1)
Computation of the SCCs
155(1)
Computation of the bow-tie regions
156(1)
Disjoint bipartite cliques
157(3)
PageRank
160(1)
Summary and outlook
161(1)
The Large Scale Structure of the Internet
162(23)
Introduction
163(1)
Internet maps
164(4)
Heavy tailed distributions
168(3)
Sampling biases and the scale-free nature of the Internet
171(2)
Hierarchies and correlations
173(10)
Outlook
183(2)
Spanning Trees in Ecology
185(20)
Introduction
185(1)
Graph-theoretical formalism
186(3)
Basic notions
187(1)
Connected subgraphs and minimum spanning trees
188(1)
Graphs and spanning trees in ecology
189(6)
Spanning trees in food webs
189(3)
Spanning trees in taxonomy
192(3)
Empirical results
195(7)
Food webs
195(3)
Taxonomic trees
198(4)
Summary and outlook
202(3)
Social and Financial Networks
205(30)
Introduction
205(1)
Social networks: Examples and general features
206(2)
Degree distribution
208(2)
Open questions on degree distribution
210(1)
Assortativity
210(3)
Open questions on assortativity
212(1)
Clustering
213(1)
Open questions on clustering
214(1)
Community structure
214(4)
Open questions on community structure
216(2)
Economical networks: The case-study of the board of directors
218(4)
Board and directors network as bipartite graphs
220(2)
Topological properties of boards and directors Networks
222(6)
Average quantities
222(2)
Degree distributions and assortativity
224(2)
Lobbies
226(2)
Modeling boards of directors networks
228(5)
Interlock structure and decision making dynamics
228(1)
Single board decision making model
229(3)
Multiple boards decision making model
232(1)
Conclusion
233(2)
References 235(14)
Index 249


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