Behavior and Evolutionary Dynamics in Crowd Networks: An Evolutionary Game Approach

ISBN-13: 9789811571626 / Angielski / Miękka / 2021 / 135 str.

Yan Chen; H. Vicky Zhao

Spis treści

Introduction.- Evolutionary Dynamics with Rational Users.- “Irrational” Behavior Analysis.- “Smart” Evolution with Indirect Reciprocity.- User Heterogeneity.- Evolution of Correlated Events.- Concluding Remarks.

O autorze

Yan Chen received the bachelor's degree from the University of Science and Technology of China in 2004, the M.Phil. degree from the Hong Kong University of Science and Technology in 2007, and the Ph.D. degree from the University of Maryland, College Park, MD, USA, in 2011. He was with Origin Wireless Inc. as a Founding Principal Technologist. From Sept. 2015 to Feb. 2020, he was a full Professor with the School of Information and Communication Engineering at the University of Electronic Science and Technology of China. He is currently a full Professor with the School of Cyberspace Security at the University of Science and Technology of China. His research interests include multimedia, signal processing, game theory, and wireless communications.

He was the recipient of multiple honors and awards, including the best student paper award at the PCM in 2017, best student paper award at the IEEE ICASSP in 2016, the best paper award at the IEEE GLOBECOM in 2013, the Future Faculty Fellowship and Distinguished Dissertation Fellowship Honorable Mention from the Department of Electrical and Computer Engineering in 2010 and 2011, the Finalist of the Dean's Doctoral Research Award from the A. James Clark School of Engineering, the University of Maryland in 2011, and the Chinese Government Award for outstanding students abroad in 2010.

H. Vicky Zhao received the B.S. and M.S. degree from Tsinghua University, China, in 1997 and 1999, respectively, and the Ph.D. degree from the University of Maryland, College Park, in 2004, all in Electrical Engineering. She was a Research Associate with the Department of Electrical and Computer Engineering and the Institute for Systems Research, University of Maryland, College Park, from January 2005 to July 2006. From August 2006 to February 2016, she was with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada, as an Assistant Professor (2006–2012) and then an Associate Professor (2012–2016). Since May 2016, she has been an Associate Professor with the Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.

Dr. Zhao’s research interests include media-sharing social networks, information security and forensics, digital communications, and signal processing. Dr. Zhao received the IEEE Signal Processing Society (SPS) 2008 Young Author Best Paper. She is a co-author of “Multimedia Fingerprinting Forensics for Traitor Tracing” (Hindawi, 2005) and “Behavior Dynamics in Media-Sharing Social Networks” (Cambridge University Press, 2011). She was a member of IEEE Signal Processing Society Information Forensics and Security Technical Committee and Multimedia Signal Processing Technical Committee.  She is the Senior Area Editor and Associate Editor for IEEE Information Forensics and Security, IEEE Signal Processing Letters, and Elsevier Journal of Visual Communication and Image Representation and a guest editor of IEEE Signal Processing Magazine. Dr. Zhao also actively participates in organizing international conferences as General Co-Chair, Technical Program Co-Chair, Finance Co-Chair, etc.

Blurb

This book offers a holistic framework to study behavior and evolutionary dynamics in large-scale, decentralized, and heterogeneous crowd networks. It reviews the fundamental methodologies to study user interactions and evolutionary dynamics in crowd networks and discusses recent advances in this emerging interdisciplinary research field. Using information diffusion over social networks as an example, it presents a thorough investigation of the impact of user behavior on the network evolution process and demonstrates how this can help improve network performance.