Springer Behavior and Evolutionary Dynamics in Crowd Networks: An Evolutionary Game Approach (Lecture Notes in Social Networks)

This book offers a holistic framework to study behavior and evolutionary dynamics in large-scale, decentralized, and heterogeneous crowd networks. In the emerging crowd cyber-ecosystems, millions of deeply connected individuals, smart devices, government agencies, and enterprises actively interact with each other and influence each other’s decisions. It is crucial to understand such intelligent entities’ behaviors and to study their strategic interactions in order to provide important guidelines on the design of reliable networks capable of predicting and preventing detrimental events with negative impacts on our society and economy.This book 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. Intended for graduate students and researchers from various disciplines, including but not limited to, data science, networking, signal processing, complex systems, and economics, the book encourages researchers in related research fields to explore the many untouched areas in this domain, and ultimately to design crowd networks with efficient, effective, and reliable services. From the Back Cover 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.  About the Author 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 Al