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"In large-scale media-sharing social networks, where millions of users create, share, link and reuse media content, there are clear challenges in protecting content security and intellectual property, and in designing scalable and reliable networks capable of handling high levels of traffic. This comprehensive resource demonstrates how game theory can be used to model user dynamics and optimize design of media-sharing networks. It reviews the fundamental methodologies used to model and analyze human behavior, using examples from real-world multimedia social networks. With a thorough investigation of the impact of human factors on multimedia system design, this accessible book shows how an understanding of human behavior can be used to improve system performance. Bringing together mathematical tools and engineering concepts with ideas from sociology and human behavior analysis, this one-stop guide will enable researchers to explore this emerging field further and ultimately design media-sharing systems with more efficient, secure and personalized services"--

Intro -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- 1 Introduction -- 1.1 Crowd Networks -- 1.2 Information Diffusion -- 1.3 Evolutionary Game Theory -- 1.4 About This Book -- References -- 2 Evolutionary Dynamics with Rational Users -- 2.1 Introduction -- 2.2 Graphical Evolutionary Game Formulation -- 2.3 Analysis of Homogeneous Network -- 2.3.1 Diffusion Dynamics and ESSs Over Uniform Degree Networks -- 2.3.2 Diffusion Dynamics and ESSs Over Non-uniform Degree Networks -- 2.4 Analysis of Heterogeneous Network -- 2.4.1 Theoretical Analysis for the Unknown User Type Model -- 2.4.2 Theoretical Analysis for the Known User Type Model -- 2.5 Simulations and Experiments -- 2.5.1 Simulations Over Homogeneous Network -- 2.5.2 Simulations Over Heterogeneous Network -- 2.6 Conclusions -- References -- 3 ``Irrational'' Behavior Analysis -- 3.1 Introduction -- 3.2 Evolutionary Game Theory -- 3.2.1 Strategy Updating Rules -- 3.2.2 Graphical Evolutionary Game Formulation -- 3.3 Theoretic Analysis -- 3.3.1 Evolutionary Dynamics of Type I Nodes -- 3.3.2 Evolutionary Dynamics of Type II Nodes -- 3.3.3 ESS Analysis -- 3.4 Simulation and Experiment Results -- 3.4.1 Synthetic Networks -- 3.4.2 Facebook Networks -- 3.4.3 Microblog Data Set -- 3.5 Conclusion and Future Work -- References -- 4 ``Smart'' Evolution with Indirect Reciprocity -- 4.1 Introduction -- 4.2 System Model -- 4.2.1 Social Network Model -- 4.2.2 Payoffs and Fitness -- 4.2.3 Strategy Updating Rules -- 4.2.4 Social Norm -- 4.2.5 Reputation Updating Policy -- 4.3 Information Diffusion Model Based on Indirect Reciprocity -- 4.3.1 Analysis for Type i Rational Users -- 4.3.2 Analysis for Type ii Rational Users -- 4.3.3 Expectation of Reputation Values -- 4.4 Simulations and Experiments -- 4.4.1 Synthetic Networks -- 4.4.2 Real-World Data -- 4.5 Conclusion and Future Work.