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BL
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| Record Number
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865386
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| Title & Author
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Complex spreading phenomena in social systems : : influence and contagion in real-world social networks /\ Sune Lehmann, Yong-Yeol Ahn, editors.
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| Publication Statement
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Cham :: Springer,, [2018]
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| Series Statement
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Computational social sciences
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| Page. NO
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1 online resource (vi, 361 pages)
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| ISBN
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3319773321
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: 9783319773322
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3319773313
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9783319773315
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Contents; Part I Introduction to Spreading in Social Systems; Complex Contagions: A Decade in Review; 1 Introduction; 2 Empirical Advances; 2.1 Applications to Health; 2.2 Diffusion of Innovations; 2.3 Social Media; 2.4 Politics; 3 Theoretical Advances; 4 New Directions; 4.1 Ecologies of Complex Contagions; 4.2 Mapping Heterogeneous Thresholds in Context; 4.3 The Roles of Homophily and Diversity in Diffusion; 5 Conclusion; References; A Simple Person's Approach to Understanding the Contagion Condition for Spreading Processes on Generalized Random Networks; 1 Introduction.
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2 Elements of Simple Contagion on Random Networks3 The Contagion Condition; 3.1 Contagion Condition for One-Shot Spreading Processes; 3.2 Contagion Condition for Multiple-Shot Spreading Processes; 3.3 Remorseless Spreading and the Giant Component Condition; 3.4 Simple Contagion on Generalized Random Networks; 3.5 Other Routes to Determining the Contagion and Giant Component Conditions; 3.6 Simple Contagion on Generalized Directed Random Networks; 3.7 Simple Contagion on Mixed, Correlated Random Networks; 3.8 Contagion on Correlated Random Networks with Arbitrary Node and Edge Types.
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2.2 Message-Passing for Configuration-Model Networks2.3 The Criticality Condition (i.e., ``Cascade Condition''); 3 Networks with Degree-Degree Correlations; 3.1 Matrix Criticality Condition; 4 Message-Passing for Finite-Size Networks; 4.1 Criticality Condition for Finite-Size Networks; 5 Conclusions; References; Optimal Modularity in Complex Contagion; 1 Introduction; 2 Analytical Framework; 2.1 Mean-Field and Message-Passing Approaches for Configuration Model; 2.2 Generalization to Modular Networks; 3 Networks with Two Communities; 4 Optimal Modularity in Networks with Many Communities.
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3.9 Simple Contagion on Bipartite Random Networks3.10 Threshold Contagion on Generalized Random Networks; 3.11 Connecting the Contagion Condition for All-To-All and Random Networks for Threshold Contagion; 4 Concluding Remarks; References; Challenges to Estimating Contagion Effects from Observational Data; 1 Background; 2 Motivating Example; 3 Defining Causal Effects; 4 Confounding; 4.1 Homophily; 4.2 Shared Environment; 5 Dependence; 5.1 Sources of Network Dependence; 6 Solutions; 6.1 Randomization; 6.2 Parametric Models; 6.3 Instrumental Variable Methods.
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6.4 Data from Multiple Independent Networks6.5 Highly Structured Dependence; 7 Conclusion; References; Part II Models and Theories; Slightly Generalized Contagion: Unifying Simple Models of Biological and Social Spreading; 1 Introduction; 2 Independent Interaction Models of Biological Contagion; 3 Interdependent Interaction Models of Social Contagion; 4 Generalized Contagion Model; 5 Analysis; 6 Concluding Remarks; References; Message-Passing Methods for Complex Contagions; 1 Introduction; 2 Configuration-Model Networks; 2.1 Naive Mean-Field Approximation.
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| Abstract
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This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches. This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state-of-the-art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily. Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.
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| Subject
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Computational complexity.
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System analysis.
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Computational complexity.
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Computer modelling simulation.
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Data mining.
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Mathematical physics.
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SCIENCE-- System Theory.
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Social research statistics.
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System analysis.
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TECHNOLOGY ENGINEERING-- Operations Research.
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| Dewey Classification
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003
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| LC Classification
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QA402
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| Added Entry
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Ahn, Yong-Yeol
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Lehmann, Sune
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