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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/social-economic-networks
课程评论：没有评论
课程名称：社会与经济网络：模型与分析 课程概述：本课程旨在教授如何建模社会和经济网络，以及这些网络对人类行为的影响。我们将探讨网络是如何形成的、为何会呈现某些模式，以及它们的结构如何影响传播、学习和其他行为。课程将结合经济学、社会学、数学、物理学、统计学和计算机科学的模型与技术，来解答这些问题。 课程内容包括： - 引言、实证背景与定义：社会网络示例及其影响，网络的定义、度量及属性，如度、直径、小世界、弱强联系及度分布。 - 背景、定义与度量延续：同类性、动态性，中心性度量（度中心性、中介中心性、接近中心性、特征向量中心性和Katz-Bonacich中心性），Erdos和Renyi随机网络的阈值与相变。 - 随机网络：泊松随机网络、指数随机图模型、增长随机网络、优先连接和幂律，网络形成的混合模型。 - 战略网络形成：网络形成的博弈论建模，连接模型，激励与效率之间的冲突，动态性、导向网络、选择与机遇的混合模型。 - 网络上的传播：实证背景，Bass模型，传染的随机网络模型，SIS模型，拟合模拟模型与数据。 - 网络学习：网络上的贝叶斯学习，DeGroot学习模型，信念的收敛，集体智慧，影响如何依赖于网络位置。 - 网络游戏：网络游戏，同行影响：战略互补与替代，网络结构与行为的关系，一个线性二次游戏，重复交互与网络结构。 - 期末考试。 课程的详细大纲和视频介绍可在以下链接找到： 大纲链接：http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf 视频链接：http://web.stanford.edu/~jacksonm/Intro_Networks.mp4
Name:Introduction, Empirical Background and Definitions
Description:Examples of Social Networks and their Impact, Definitions, Measures and Properties: Degrees, Diameters, Small Worlds, Weak and Strong Ties, Degree Distributions
Name:Background, Definitions, and Measures Continued
Description:Homophily, Dynamics, Centrality Measures: Degree, Betweenness, Closeness, Eigenvector, and Katz-Bonacich. Erdos and Renyi Random Networks: Thresholds and Phase Transitions
Name:Random Networks
Description:Poisson Random Networks, Exponential Random Graph Models, Growing Random Networks, Preferential Attachment and Power Laws, Hybrid models of Network Formation.
Name:Strategic Network Formation
Description:Game Theoretic Modeling of Network Formation, The Connections Model, The Conflict between Incentives and Efficiency, Dynamics, Directed Networks, Hybrid Models of Choice and Chance.
Name:Diffusion on Networks
Description:Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.
Name:Learning on Networks
Description:Bayesian Learning on Networks, The DeGroot Model of Learning on a Network, Convergence of Beliefs, The Wisdom of Crowds, How Influence depends on Network Position..
Name:Games on Networks
Description:Network Games, Peer Influences: Strategic Complements and Substitutes, the Relation between Network Structure and Behavior, A Linear Quadratic Game, Repeated Interactions and Network Structures.
Name:Final Exam
Description:The description goes here
Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences. You can find a more detailed syllabus here: http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4
