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Introduction to Complex Networks：Models, Structures and Dynamics (2nd)(复杂网络引论)

样章
作者：
陈关荣 汪小帆 李翔
定价：
79.00元
ISBN：
978-7-04-040605-4
版面字数：
480.000千字
开本：
16开
全书页数：
366页
装帧形式：
精装
重点项目：
暂无
出版时间：
2015-01-13
读者对象：
学术著作
一级分类：
自然科学
二级分类：
交叉学科

《Introduction to Complex Networks：Models, Structu》是为自然科学、数学和工程领域的研究生以及本科高年级学生拟写的一本入门书，可以作为一个学期教学使用的参考讲义，也可以作为科研参考书或自学读物。

《Introduction to Complex Networks：Models, Structu》力求正确和准确，但并不刻意采取十分严谨的写法，以期通俗易懂，侧重于主要思想和基本方法的介绍，仅提供启发性的数学支撑，希望具有初等微积分、线性代数和常微分方程的读者能够轻松地学习书中的主要内容。

《Introduction to Complex Networks：Models, Structu》分成两大部分：第一部分是基础理论，提供足够的背景材料和信息并附有适量的练习题，旨在让读者熟悉一些最基本的建模方法和分析技巧。第二部分是应用选题，包括复杂网络在几个代表性领域中的应用研究。这些章节彼此相对独立。最后一章是近年来比较活跃的几个前沿研究课题的简介。各大章均附有详细的关键文献，希望能帮助有兴趣的读者很快地进入这些研究领域。

  • 前辅文
  • 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 Brief Introduction to Other Topics
      • 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

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