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Behavior Dynamics in Media-Sharing Social Networks [Hardback]

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(University of Maryland, College Park), (University of Maryland, College Park), (University of Alberta)
  • Formāts: Hardback, 350 pages, height x width x depth: 254x178x21 mm, weight: 840 g, 7 Tables, black and white; 5 Halftones, unspecified; 105 Line drawings, unspecified
  • Izdošanas datums: 14-Apr-2011
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521197279
  • ISBN-13: 9780521197274
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  • Hardback
  • Cena: 121,03 €
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Behavior Dynamics in Media-Sharing Social NetworksPalielināt
  • Formāts: Hardback, 350 pages, height x width x depth: 254x178x21 mm, weight: 840 g, 7 Tables, black and white; 5 Halftones, unspecified; 105 Line drawings, unspecified
  • Izdošanas datums: 14-Apr-2011
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521197279
  • ISBN-13: 9780521197274
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780521197274.html
Keywords:
"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"--

Provided by publisher.

Demonstrates how signal processing techniques can be used to model user dynamics and optimize design of media-sharing network systems.

Recenzijas

' the authors provide mathematical means for analysis of socially-enabled media sharing networks and their performance one may find inspiration in the book.' IEEE Communications Magazine

Papildus informācija

Demonstrates how signal processing techniques can be used to model user dynamics and optimize design of media-sharing network systems.
Preface xi
Part I Introduction
1(54)
1 Introduction to media-sharing social networks
3(11)
1.1 Quantitative analysis of social networks
5(5)
1.2 Understanding media semantics in media-sharing networks
10(4)
2 Overview of multimedia fingerprinting
14(10)
2.1 Traitor-tracing multimedia fingerprinting
15(2)
2.2 Scalable video coding system
17(1)
2.3 Scalable video fingerprinting
18(6)
3 Overview of mesh-pull peer-to-peer video streaming
24(17)
3.1 Mesh-pull structure for P2P video streaming
25(8)
3.2 User dynamics in peer-to-peer video streaming
33(8)
4 Game theory for social networks
41(14)
4.1 Noncooperative and cooperative games
42(1)
4.2 Noncooperative games
43(7)
4.3 Bargaining games
50(5)
Part II Behavior forensics in media-sharing social networks
55(74)
5 Equal-risk fairness in colluder social networks
57(28)
5.1 Equal-risk collusion
57(6)
5.2 Influence on the detector's side: collusion resistance
63(12)
5.3 Traitor-tracing capability of scalable fingerprints
75(7)
5.4
Chapter summary and bibliographical notes
82(3)
6 Leveraging side information in colluder social networks
85(26)
6.1 Probing and using side information
85(8)
6.2 Game-theoretic analysis of colluder detector dynamics
93(1)
6.3 Equilibrium analysis
94(9)
6.4 Simulation results
103(6)
6.5
Chapter summary and bibliographical notes
109(2)
7 Risk--distortion analysis of multiuser collusion
111(18)
7.1 Video fingerprinting
112(1)
7.2 Risk--distortion modeling
113(4)
7.3 Strategies with side information
117(5)
7.4 Parameter estimation
122(1)
7.5 Simulation results
122(5)
7.6
Chapter summary and bibliographical notes
127(2)
Part III Fairness and cooperation stimulation
129(90)
8 Game-theoretic modeling of colluder social networks
131(38)
8.1 Multiuser collusion game
132(5)
8.2 Feasible and Pareto optimal collusion
137(2)
8.3 When to collude
139(11)
8.4 How to collude: the bargaining model
150(5)
8.5 How to collude: examples
155(5)
8.6 Maximum payoff collusion
160(7)
8.7
Chapter summary and bibliographical notes
167(2)
9 Cooperation stimulation in peer-to-peer video streaming
169(26)
9.1 Incentives for peer cooperation over the Internet
170(8)
9.2 Wireless peer-to-peer video streaming
178(3)
9.3 Optimal cooperation strategies for wireless video streaming
181(8)
9.4 Optimal chunk request algorithm for P2P video streaming with scalable coding
189(4)
9.5
Chapter summary and bibliographical notes
193(2)
10 Optimal pricing for mobile video streaming
195(24)
10.1 Introduction
195(1)
10.2 System model
196(2)
10.3 Optimal strategies for single secondary buyer
198(8)
10.4 Multiple secondary buyers
206(2)
10.5 Optimal pricing for the content owner
208(9)
10.6
Chapter summary and bibliographical notes
217(2)
Part IV Misbehaving user identification
219(56)
11 Cheating behavior in colluder social networks
221(26)
11.1 Traitors within traitors via temporal filtering
222(5)
11.2 Traitors within traitors in scalable fingerprinting systems
227(18)
11.3
Chapter summary
245(2)
12 Attack resistance in peer-to-peer video streaming
247(28)
12.1 Attack-resistant cooperation strategies in P2P video streaming over the Internet
248(13)
12.2 Attack-resistant cooperation strategies in wireless P2P video streaming
261(12)
12.3
Chapter summary and bibliographical notes
273(2)
Part V Media-sharing social network structures
275(51)
13 Misbehavior detection in colluder social networks with different structures
277(31)
13.1 Behavior dynamics in colluder social networks
278(2)
13.2 Centralized colluder social networks with trusted ringleaders
280(9)
13.3 Distributed peer-structured colluder social networks
289(17)
13.4
Chapter summary and bibliographical notes
306(2)
14 Structuring cooperation for hybrid peer-to-peer streaming
308(18)
14.1 System model and utility function
309(2)
14.2 Agent selection within a homogeneous group
311(6)
14.3 Agent selection within a heterogeneous group
317(3)
14.4 Distributed learning algorithm for ESS
320(1)
14.5 Simulation results
320(5)
14.6
Chapter summary and bibliographical notes
325(1)
References 326(9)
Index 335
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. 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.