Behavior Dynamics in Media-Sharing Social Networks

Name: Behavior Dynamics in Media-Sharing Social Networks
Price: $118.88 USD
Availability: InStock
Author: H. Vicky Zhao , W. Sabrina Lin , K. J. Ray Liu
ISBN: 9780521197274

by H. Vicky Zhao , W. Sabrina Lin , K. J. Ray Liu

ISBN13:
9780521197274
ISBN10:
0521197279
Format: Hardcover
Copyright: 2011-05-16
Publisher: Cambridge University Press
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Summary

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.

Author Biography

H.Vicky Zhao is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Alberta. The recipient of the IEEE Signal Processing Society - Young Author Best Paper Award 2008, she is an Associate Editor for the IEEE Signal Processing Letters and the Journal of Visual Communication and Image Representation. W. Sabrina Lin is a Research Associate in the Department of Electrical and Computer Engineering at the University of Maryland. She received the University of Maryland Future Faculty Fellowship in 2007. K. J. Ray Liu is a Distinguished Scholar-Teacher of the University of Maryland, where he is Christine Kim Eminent Professor in Information Technology. He received the IEEE Signal Processing Society Technical Achievement Award in 2009, and was Editor-in-Chief of the IEEE Signal Processing Magazine and the founding Editor-in-Chief of the EURASIP Journal on Advances in Signal Processing.

Preface	p. xi
Introduction	p. 1
Introduction to media-sharing social networks	p. 3
Quantitative analysis of social networks	p. 5
Understanding media semantics in media-sharing networks	p. 10
Overview of multimedia fingerprinting	p. 14
Traitor-tracing multimedia fingerprinting	p. 15
Scalable video coding system	p. 17
Scalable video fingerprinting	p. 18
Overview of mesh-pull peer-to-peer video streaming	p. 24
Mesh-pull structure for P2P video streaming	p. 25
User dynamics in peer-to-peer video streaming	p. 33
Game theory for social networks	p. 41
Noncooperative and cooperative games	p. 42
Noncooperative games	p. 43
Bargaining games	p. 50
Behavior forensics in media-sharing social networks	p. 55
Equal-risk fairness in colluder social networks	p. 57
Equal-risk collusion	p. 57
Influence on the detector's side: collusion resistance	p. 63
Traitor-tracing capability of scalable fingerprints	p. 75
Chapter summary and bibliographical notes	p. 82
Leveraging side information in colluder social networks	p. 85
Probing and using side information	p. 85
Game-theoretic analysis of colluder detector dynamics	p. 93
Equilibrium analysis	p. 94
Simulation results	p. 103
Chapter summary and bibliographical notes	p. 109
Risk-distortion analysis of multiuser collusion	p. 111
Video fingerprinting	p. 112
Risk-distortion modeling	p. 113
Strategies with side information	p. 117
Parameter estimation	p. 122
Simulation results	p. 122
Chapter summary and bibliographical notes	p. 127
Fairness and cooperation stimulation	p. 129
Game-theoretic modeling of colluder social networks	p. 131
Multiuser collusion game	p. 132
Feasible and Pareto optimal collusion	p. 137
When to collude	p. 139
How to collude: the bargaining model	p. 150
How to collude: examples	p. 155
Maximum payoff collusion	p. 160
Chapter summary and bibliographical notes	p. 167
Cooperation stimulation in peer-to-peer video streaming	p. 169
Incentives for peer cooperation over the Internet	p. 170
Wireless peer-to-peer video streaming	p. 178
Optimal cooperation strategies for wireless video streaming	p. 181
Optimal chunk request algorithm for P2P video streaming with scalable coding	p. 189
Chapter summary and bibliographical notes	p. 193
Optimal pricing for mobile video streaming	p. 195
Introduction	p. 195
System model	p. 196
Optimal strategies for single secondary buyer	p. 198
Multiple secondary buyers	p. 206
Optimal pricing for the content owner	p. 208
Chapter summary and bibliographical notes	p. 217
Misbehaving user identification	p. 219
Cheating behavior in colluder social networks	p. 221
Traitors within traitors via temporal filtering	p. 222
Traitors within traitors in scalable fingerprinting systems	p. 227
Chapter summary	p. 245
Attack resistance in peer-to-peer video streaming	p. 247
Attack-resistant cooperation strategies in P2P video streaming over the Internet	p. 248
Attack-resistant cooperation strategies in wireless P2P video streaming	p. 261
Chapter summary and bibliographical notes	p. 273
Media-sharing social network structures	p. 275
Misbehavior detection in colluder social networks with different structures	p. 277
Behavior dynamics in colluder social networks	p. 278
Centralized colluder social networks with trusted ringleaders	p. 280
Distributed peer-structured colluder social networks	p. 289
Chapter summary and bibliographical notes	p. 306
Structuring cooperation for hybrid peer-to-peer streaming	p. 308
System model and utility function	p. 309
Agent selection within a homogeneous group	p. 311
Agent selection within a heterogeneous group	p. 317
Distributed learning algorithm for ESS	p. 320
Simulation results	p. 320
Chapter summary and bibliographical notes	p. 325
References	p. 326
Index	p. 335
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