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EBOOK
Title Diffusion in social networks / Paulo Shakarian, Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo.
Imprint Cham : Springer, 2015.

LOCATION CALL # STATUS MESSAGE
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LOCATION CALL # STATUS MESSAGE
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Series SpringerBriefs in computer science, 2191-5768
SpringerBriefs in computer science.
Subject Diffusion processes.
Social networks -- Mathematical models.
Social networks -- Computer simulation.
Alt Name Shakarian, Paulo,
Bhatnagar, Abhinav,
Aleali, Ashkan,
Shaabani, Elham,
Guo, Ruocheng,
Description 1 online resource (xi, 101 pages) : illustrations (some color).
polychrome rdacc
Bibliography Note Includes bibliographical references.
Contents Introduction -- The SIR Model and Identification of Spreaders -- The Tipping Model and the Minimum Seed Problem -- The Independent Cascade and Linear Threshold Models -- Logic Programming Based Diffusion Models -- Evolutionary Graph Theory -- Examining Diffusion in the Real World -- Conclusion.
Machine generated contents note: 1. Introduction -- References -- 2. SIR Model and Identification of Spreaders -- 2.1. Introduction -- 2.2. SIR Model -- 2.2.1. Selecting the Infection Probability -- 2.3. Centrality and Other Nodal Measures -- 2.3.1. Degree Centrality -- 2.3.2. Shell Number -- 2.3.3. Betweenness Centrality -- 2.3.4. Closeness Centrality -- 2.3.5. Eigenvector Centrality -- 2.3.6. PageRank -- 2.3.7. Neighborhood -- 2.3.8. Imprecision Functions -- 2.4. Experimental Findings -- 2.4.1. Datasets -- 2.4.2. Sensitivity to β -- 2.4.3. Eigenvector Centrality for Spreader Identification -- 2.4.4. Large Values of β -- 2.5. Conclusions -- References -- 3. Tipping Model and the Minimum Seed Problem -- 3.1. Introduction -- 3.2. Tipping Model -- 3.3. Minimum Seed Problem -- 3.3.1. Exact Approach -- 3.3.2. Heuristic -- 3.4. Experimental Findings -- 3.4.1. Datasets -- 3.4.2. Runtime -- 3.4.3. Seed Size -- 3.4.4. Comparison with Centrality Measures -- 3.4.5. Effect of Removing High-Degree Nodes -- 3.5. Conclusion -- References -- 4. Independent Cascade and Linear Threshold Models -- 4.1. Introduction -- 4.2. Model Definitions -- 4.2.1. Independent Cascade Model -- 4.2.2. Linear Threshold Model -- 4.2.3. Generalized Threshold Model -- 4.3. Influence Maximization Problem -- 4.3.1. Influence Maximization Under the IC Model -- 4.3.2. Influence Maximization Under the LT Model -- 4.3.3. Influence Maximization Under the GT Model -- 4.4. Scaling Influence Maximization -- 4.4.1. Lazy Greedy Approximation -- 4.4.2. Maximum Influence Arborescence (MIA) Model -- 4.4.3. SIMPATH Algorithm -- 4.5. Conclusion -- References -- 5. Logic Programming Based Diffusion Models -- 5.1. Introduction -- 5.2. Embedding Diffusion Models into Annotated Logic Programs -- 5.2.1. Social Networks Formalization -- 5.2.2. Generalized Annotated Programs: A Recap -- 5.3. Social Network Diffusion Optimization Problem (SNDOP) Queries -- 5.3.1. Basic SNDOP Queries -- 5.3.2. Special Cases of SNDOPs -- 5.3.3. Complexity of SNDOP Queries -- 5.4. Applying SNDOPs to Diffusion Problems -- 5.4.1. Tipping Diffusion -- 5.4.2. Cascading Diffusion -- 5.4.3. Homophilic Diffusion -- 5.5. Algorithmic Approach and Experiments -- 5.6. Conclusion -- References -- 6. Evolutionary Graph Theory -- 6.1. Introduction -- 6.2. Evolutionary Graph Theory Models -- 6.2.1. Properties of Fixation Probability -- 6.2.2. Game Theoretic Extensions -- 6.3. Determining Fixation Probability for Fixed Fitness -- 6.3.1. Fixation Probability Calculations for Certain Topologies -- 6.3.2. Undirected Evolutionary Graphs -- 6.4. Alternate Update Rules -- 6.5. Further Game Theoretic Results -- 6.5.1. Evolutionary Stability on Graphs -- 6.5.2. Regular Graphs and the Replicator Equation -- 6.5.3. Evolution of Cooperation and Social Viscosity -- 6.5.4. Graph Heterogeneity and Evolution of Cooperation -- 6.6. Conclusion -- References -- 7. Examining Diffusion in the Real World -- 7.1. Introduction -- 7.2. Identifying Viral Diffusion Processes: Centrality-Based Approaches -- 7.3. Structural Diversity and Diffusion -- 7.4. Conclusion -- References.
Summary This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.
Note Online resource; title from PDF title page (SpringerLink, viewed September 24, 2015).
ISBN 9783319231051 (electronic bk.)
3319231057 (electronic bk.)
9783319231044
3319231049
ISBN/ISSN 10.1007/978-3-319-23105-1
OCLC # 921843506
Additional Format Print version: Diffusion in social networks. Cham : Springer, 2015 3319231049 9783319231044 (OCoLC)913767538


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