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复杂网络传播动力学:模型、方法与稳定性分析(英文版)

复杂网络传播动力学:模型、方法与稳定性分析(英文版)

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作 者: 傅新楚,[澳] 斯摩尔(Michael Small),陈关荣 著
出版社: 高等教育出版社
丛编项: 网络科学与工程丛书
标 签: 计算机/网络 网络配置与管理 网络与数据通信

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ISBN: 9787040307177 出版时间: 2014-02-01 包装: 精装
开本: 16开 页数: 314 字数:  

内容简介

《网络科学与工程丛书·复杂网络传播动力学:模型、方法与稳定性分析(英文版)》较全面介绍了复杂网络传播动力学的研究结果与最新进展,包括三位作者的近期研究成果。主要内容包括各种“仓室”模型及有关的主要结果;复杂网络上流行病的网络建模、数值模拟、疾病控制与风险评估;流行病阈值的几种主要求法;疾病的控制与免疫方法,各种免疫策略的比较;人类意识在疾病传播与控制中的独特作用;在传播媒介作用下非齐次网络上流行病动力学的性质;几种典型网络传播模型的全局稳定性;网络同步动力学与流行病传播动力学之间的自适应演化关系;信息、舆情、谣言等在因特网及社会网络中的传播特性;信启、传播与流行病传播之间的异同等。
  《网络科学与工程丛书·复杂网络传播动力学:模型、方法与稳定性分析(英文版)》可作为高等院校应用数学、生物数学、计算机科学与技术等专业相关课程的教学参考书,也可供从事动力系统、网络科学和复杂性科学等领域研究工作的教学科研人员参考使用。

作者简介

傅新楚,2001年获英国Exeter大学应用数学博士学位。1997年至2002年在英国剑桥大学、Warwick大学作高级访问学者,随后在英国Surrey大学、Exeter大学任ResearchFellow,由英国国家基金EPSRC资助研究一类不连续系统的动力学问题。2002年5月回国,在上海大学数学系工作,任教授、博士生导师。先后主持国家自然科学基金项目5项,曾参加国家“攀登计划”重大项目。
  
  斯摩尔(Michael Small),西澳大利亚大学应用数学Winthrop教授,澳大利亚研究理事会未来研究员,IEEE高级会员,澳大利亚数学会会员,多家国际期刊的编委。曾在香港理T.大学电子及信息工程系做博士后并任教。在混沌、非线性时间序列建模、复杂系统等领域的基础理论及应用方面,发表约150篇期刊论文和书籍章节,约150篇会议论文,3部著作。
  
  陈关荣,1981年获中山大学计算数学硕士学位,1987年获美国德克萨斯A&M大学应用数学博士学位。于休斯顿大学任教至2000年,现任香港城市大学电子工程系讲座教授。1996年当选为IEEE Fellow。获2012年及2008年国家自然科学二等奖、2010年何梁何利奖、2011年俄罗斯欧拉奖并获俄罗斯圣彼得堡国立大学荣誉博士学位,获5项IEEE等最佳学术杂志论文奖,是国内外30多所大学的荣誉或客座教授,现任International Journal of Bifurcation and Chaos主编。SCI他引两万多次,h指数78,被ISI评定为工程学高引用率研究人员。

图书目录

1 Introduction
1.1 Motivation and background
1.2 A brief history of mathematical epidemiology
1.2.1 Compartmental modeling
1.2.2 Epidemic modeling on complex networks
1.3 Organization of the book
References

2 Various epidemic models on complex networks
2.1 Multiple stage models
2.1.1 Multiple susceptible individuals
2.1.2 Multiple infected individuals
2.1.3 Multiple-staged infected individuals
2.2 Staged progression models2.1 Multiple stage models
2.1:1 Multiple susceptible individuals
2.1.2 Multiple infected individuals
2.1.3 Multiple-staged infected individuals
2.2 Staged progression models
2.2.1 Simple-staged progression model
2.2.2 Staged progression model on homogenous networks
2.2.3 Staged progression model on heterogenous networks
2.2.4 Staged progression model with birth and death
2.2.5 Staged progression model with birth and death on homogenous networks
2.2.6 Staged progression model with birth and death on heterogenous networks
2.3 Stochastic SIS model
2.3.1 A general concept: Epidemic spreading efficiency
2.4 Models with population mobility
2.4.1 Epidemic spreading without mobility of individuals
2.4.2 Spreading of epidemic diseases among different cities
2.4.3 Epidemic spreading within and between cities
2.5 Models in meta-populations
2.5.1 Model formulation
2.6 Models with effective contacts
2.6.1 Epidemics with effectively uniform contact
2.6.2 Epidemics with effective contact in homogenous and heterogenous networks
2.7 Models with two distinct routes
2.8 Models with competing strains
2.8.1 SIS model with competing strains
2.8.2 Remarks and discussions
2.9 Models with competing strains and saturated infectivity
2.9.1 SIS model with mutation mechanism
2.9.2 SIS model with super-infection mechanism
2.10 Models with birth and death of nodes and links
2.11 Models on weighted networks
2.11.1 Model with birth and death and adaptive weights
2.12 Models on directed networks
2.13 Models on colored networks
2.13.1 SIS epidemic models on colored networks
2.13.2 Microscopic Markov-chain analysis
2.14 Discrete epidemic models
2.14.1 Discrete SIS model with nonlinear contagion scheme
2.14.2 Discrete-time epidemic model in heterogenous networks
2.14.3 A generalized model References

3 Epidemic threshold analysis
3.1 Threshold analysis by the direct method
3.1.1 The epidemic rate is βln inside the same cities
3.1.1 Epidemics on homogenous networks
3.1.1 Epidemics on heterogenous networks
3.2 Epidemic spreading efficiency threshold and epidemic threshold
3.2.1 The case of λ1 ≠λ2
3.2.2 The case of λ1≠λ2
3.2.3 Epidemic threshold in finite populations
3.2.4 Epidemic threshold in infinite populations
3.3 Epidemic thresholds and basic reproduction numbers
3.3.1 Threshold from a self-consistency equation
3.3.2 Threshold unobtainable from a self-consistency equation
3.3.3 Threshold analysis for SIS model with mutation
3.3.4 Threshold analysis for SIS model with super-infection
3.3.5 Epidemic thresholds for models on directed networks
3.3.6 Epidemic thresholds on technological and social networks
3.3.7 Epidemic thresholds on directed networks with immunization
3.3.8 Comparisons of epidemic thresholds for directed networks with immunizationdel
……
4 Networked models for SARS and avian influenza
5 Infectivity functions
6 SIS models with an infective medium
7 Epidemic control and awareness
8 Adaptive mechanism between dynamics and epidemics
9 Epidemic control and immunization
10 Global stability analysis
11 Information diffusion and pathogen propagation
Appendix A Proofs of theorems
Appendix B Further proofs of results
Index

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