内容简介：　　　

  Complex networks such as the Internet, WWW, transportation networks, power grids, biological neural networks, and scientific cooperation networks of all kinds provide challenges for future technological development. 
  The first systematic presentation of dynamical evolving networks, with many up-to-date applications and homework projects to enhance study. The authors are all very active and well-known in the rapidly evolving field of complex networks. Complex networks are becoming an increasingly important area of research. Presented in a logical, constructive style, from basic through to complex, examining algorithms, through to construct networks and research challenges of the future

英文目录：
Part I FUNDAMENTAL THEORY
1 Introduction
　　1.1 Background and Motivation
　　1.2 A Brief History of Complex Network Research
　　　　1.2.1 The Königsburg Seven-Bridge Problem
　　　　1.2.2 Random Graph Theory
　　　　1.2.3 Small-World Experiments
　　　　1.2.4 Strengths of Weak Ties
　　　　1.2.5 Heterogeneity and the WWW
　　1.3 New Era of Complex-Network Studies
　　Exercises
　　References
2 Preliminaries
　　2.1 Elementary Graph Theory
　　　　2.1.1 Background
　　　　2.1.2 Basic Concepts
　　　　2.1.3 Adjacency, Incidence and Laplacian Matrices
　　　　2.1.4 Degree Correlation and Assortativity
　　　　2.1.5 Some Basic Results on Graphs
　　　　2.1.6 Eulerian and Hamiltonian Graphs
　　　　2.1.7 Plane and Planar Graphs
　　　　2.1.8 Trees and Bipartite Graphs
　　　　2.1.9 Directed Graphs
　　　　2.1.10 Weighted Graphs
　　　　2.1.11 Some Applications
　　2.2 Elementary Probability and Statistics
　　　　2.2.1 Probability Preliminaries
　　　　2.2.2 Statistics Preliminaries
　　　　2.2.3 Law of Large Numbers and Central Limit Theorem
　　　　2.2.4 Markov Chains
　　2.3 Elementary Dynamical Systems Theory
　　　　2.3.1 Background and Motivation
　　　　2.3.2 Some Analytical Tools
　　　　2.3.3 Chaos in Nonlinear Systems
　　　　2.3.4 Kolmogorov-Sinai Entropy
　　　　2.3.5 Some Examples of Chaotic Systems
　　　　2.3.6 Stabilities of Nonlinear Systems
　　Exercises
　　References
3 Network Topologies: Basic Models and Properties
　　3.1 Introduction
　　3.2 Regular Networks
　　3.3 ER Random-Graph Model
　　3.4 Small-World Network Models
　　　　3.4.1 WS Small-World Network Model
　　　　3.4.2 NW Small-World Network Model
　　　　3.4.3 Statistical Properties of Small-World Network Models
　　3.5 Navigable Small-World Network Model
　　3.6 Scale-Free Network Models
　　　　3.6.1 BA Scale-Free Network Model
　　　　3.6.2 Robustness versus Fragility
　　　　3.6.3 Modified BA Models
　　　　3.6.4 A Simple Model with Power-Law Degree Distribution
　　　　3.6.5 Local-World and Multi-Local-World Network Models
　　Exercises
　　References
Part II APPLICATIONS - SELECTED TOPICS
4 Internet: Topology and Modeling
　　　4.1 Introduction
　　　4.2 Topological Properties of the Internet
　　　　4.2.1 Power–Law Node-Degree Distribution
　　　　4.2.2 Hierarchical Structure
　　　　4.2.3 Rich-Club Structure
　　　　4.2.4 Disassortative Property
　　　　4.2.5 Coreness and Betweenness
　　　　4.2.6 Growth of the Internet
　　　　4.2.7 Router-Level Internet Topology
　　　　4.2.8 Geographic Layout of the Internet
　　4.3 Random-Graph Network Topology Generator
　　4.4 Structural Network Topology Generators
　　　　4.4.1 Tiers Topology Generator
　　　　4.4.2 Transit–Stub Topology Generator
　　4.5 Connectivity-Based Network Topology Generators
　　　　4.5.1 Inet
　　　　4.5.2 BRITE Model
　　　　4.5.3 GLP Model
　　　　4.5.4 PFP Model
　　　　4.5.5 TANG Model
　　4.6 Multi-Local-World Model
　　　　4.6.1 Theoretical Considerations
　　　　4.6.2 Numerical Results with Comparison
　　　　4.6.3 Performance Comparison
　　4.7 HOT Model
　　4.8 Dynamical Behaviors of the Internet Topological Characteristics
　　4.9 Traffic Fluctuation on Weighted Networks
　　　　4.9.1 Weighted Networks
　　　　4.9.2 GRD Model
　　　　4.9.3 Data Traffic Fluctuations
　　References
5 Epidemic Spreading Dynamics
　　　5.1 Introduction
　　　5.2 Epidemic Threshold Theory
　　　　5.2.1 Epidemic (SI, SIS, SIR) Models
　　　　5.2.2 Epidemic Thresholds on Homogenous Networks
　　　　5.2.3 Statistical Data Analysis
　　　　5.2.4 Epidemic Thresholds on Heterogeneous Networks
　　　　5.2.5 Epidemic Thresholds on BA Networks
　　　　5.2.6 Epidemic Thresholds on Finite-Sized Scale-Free Networks
　　　　5.2.7 Epidemic Thresholds on Correlated Networks
　　　　5.2.8 SIR Model of Epidemic Spreading
　　　　5.2.9 Epidemic Spreading on Quenched Networks
　　5.3 Epidemic Spreading on Spatial Networks
　　　　5.3.1 Spatial Networks
　　　　5.3.2 Spatial Network Models for Infectious Diseases
　　　　5.3.3 Impact of Spatial Clustering on Disease Transmissions
　　　　5.3.4 Large-Scale Spatial Epidemic Spreading
　　　　5.3.5 Impact of Human Location-Specific Contact Patterns
　　5.4 Immunization on Complex Networks
　　　　5.4.1 Random Immunization
　　　　5.4.2 Targeted Immunization
　　　　5.4.3 Acquaintance Immunization
　　5.5 Computer Virus Spreading over the Internet
　　　　5.5.1 Random Constant-Spread Model
　　　　5.5.2 A Compartment-Based Model
　　　　5.5.3 Spreading Models of Email Viruses
　　　　5.5.4 Effects of Computer Virus on Network Topologies
　　References
6 Community Structures
　　　6.1 Introduction
　　　　6.1.1 Various Scenarios in Real-World Social Networks
　　　　6.1.2 Generalization of Assortativity
　　 6.2 Community Structure and Modularity
　　　　6.2.1 Community Structure
　　　　6.2.2 Modularity
　　　　6.2.3 Modularity of Weighted and Directed Networks
　　6.3 Modularity-Based Community Detecting Algorithms
　　　　6.3.1 CNM Scheme
　　　　6.3.2 BGLL Scheme
　　　　6.3.3 Multi-Slice Community Detection
　　　　6.3.4 Detecting Spatial Community Structures
　　6.4 Other Community Partitioning Schemes
　　　　6.4.1 Limitations of the Modularity Measure
　　　　6.4.2 Clique Percolation Scheme
　　　　6.4.3 Edge-Based Community Detection Scheme
　　　　6.4.4 Evaluation Criteria for Community Detection Algorithms
　　6.5 Some Recent Progress
　　References
7 Network Games
　　7.1 Introduction
　　7.2 Two-Player/Two-Strategy Evolutionary Games on Networks
　　　　7.2.1 Introduction to Games on Networks
　　　　7.2.2 Two-Player/Two-Strategy Games on Regular Lattices
　　　　7.2.3 Two-Player/Two-Strategy Games on BA Scale-Free Networks
　　　　7.2.4 Two-Player/Two-Strategy Games on Correlated Scale-Free Networks
　　　　7.2.5 Two-Player/Two-Strategy Games on Clustered Scale-Free Networks
　　7.3 Multi-Player/Two-Strategy Evolutionary Games on Networks
　　　　7.3.1 Introduction to Public Goods Game
　　　　7.3.2 Multi-Player/Two-Strategy Evolutionary Games on BA Networks
　　　　7.3.3 Multi-Player/Two-Strategy Evolutionary Games on Correlated Scale-free Networks
　　　　7.3.4 Multi-Player/Two-Strategy Evolutionary Games on Clustered Scale-free Networks
　　7.4 Adaptive Evolutionary Games on Networks
　　References
8 Network Synchronization
　　8.1 Introduction
　　8.2 Complete Synchronization of Continuous-Time Networks
　　　　8.2.1 Complete Synchronization of General Continuous-Time Networks
　　　　8.2.2 Complete Synchronization of Linearly Coupled Continuous-Time Networks
　　8.3 Complete Synchronization of Some Typical Dynamical Networks
　　　　8.3.1 Complete Synchronization of Regular Networks
　　　　8.3.2 Synchronization of Small-World Networks
　　　　8.3.3 Synchronization of Scale-Free Networks
　　　　8.3.4 Complete Synchronization of Local-World Networks
　　8.4 Phase Synchronization
　　　　8.4.1 Phase Synchronization of the Kuramoto Model
　　　　8.4.2 Phase Synchronization of Small-World Networks
　　　　8.4.3 Phase Synchronization of Scale-Free Networks
　　　　8.4.4 Phase Synchronization of Nonuniformly Coupled Networks
　　References
9 Network Control
　　9.1 Introduction
　　9.2 Spatiotemporal Chaos Control on Regular CML
　　9.3 Pinning Control of Complex Networks
　　　　9.3.1 Augmented Network Approach
　　　　9.3.2 Pinning Control of Scale-Free Networks
　　9.4 Pinning Control of General Complex Networks
　　　　9.4.1 Stability Analysis of General Networks under Pinning Control
　　　　9.4.2 Pinning and Virtual Control of General Networks
　　　　9.4.3 Pinning and Virtual Control of Scale-Free Networks
　　9.5 Time-Delay Pinning Control of Complex Networks
　　9.6 Consensus and Flocking Control
　　References
10 Finite-Horizon Filtering with Degraded Measurements
　　10.1 Human Opinion Dynamics
　　10.2 Human Mobility and Behavioral Dynamics
　　10.3 Web PageRank, SiteRank and BrowserRank
　　　　10.3.1 Methods Based on Edge Analysis
　　　　10.3.2 Methods Using Users’ Behavior Data
　　10.4 Recommendation Systems
　　10.5 Network Edge Prediction
　　10.6 Living Organisms and Bionetworks
　　10.7 Cascading Reactions on Networks
　　References
Index