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Diffusion processes in large networks have been used to model many real-world phenomena, including how rumors spread on the Internet, epidemics among human beings, emotional contagion through social networks, and even gene regulatory processes. Fundamental estimation principles and efficient algorithms for locating diffusion sources can answer a wide range of important questions, such as identifying the source of a widely spread rumor on online social networks. This book provides an overview of recent progress on source localization in large networks, focusing on theoretical principles and fundamental limits. The book covers both discrete-time diffusion models and continuous-time diffusion models. For discrete-time diffusion models, the book focuses on the Jordan infection center; for continuous-time diffusion models, it focuses on the rumor center. Most theoretical results on source localization are based on these two types of estimators or their variants. This book also includes algorithms that leverage partial-time information for source localization and a brief discussion of interesting unresolved problems in this area.
List of contents
Preface.- Acknowledgments.- Motivation and Background.- Source Localization under Discrete-Time Diffusion Models.- Source Localization under Continuous-Time Diffusion Models.- Source Localization with Partial Timestamps.- Open Questions.- Bibliography.- Authors' Biographies.
About the author
Lei Ying received his B.E. degree from Tsinghua University, Beijing, China, and his M.S. and Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. He currently is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University. His research interest is broadly in the area of stochastic networks, including cloud computing, communication networks, and social networks. He is coauthor with R. Srikant of the book Communication Networks: An Optimization, Control and Stochastic Networks Perspective, Cambridge University Press, 2014. He won the Young Investigator Award from the Defense Threat Reduction Agency (DTRA) in 2009 and NSF CAREER Award in 2010. He was the Northrop Grumman Assistant Professor in the Department of Electrical and Computer Engineering at Iowa State University from 2010 to 2012. His papers have received the best paper award at IEEE INFOCOM 2015 and the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS/IFIP Performance 2016, been selected in the ACM TKDD Special Issue "Best Papers of KDD 2016," received the WiOpt'18 Best Student Paper Award, and selected for Fast-Track Review for TNSE at IEEE INFOCOM 2018 (7 out of 312 accepted papers were invited). Kai Zhu received his B.E. degree in Electronics Engineering from Tsinghua University, Beijing, China, in 2010 and his Ph.D. in Electrical Engineering from Arizona State University in 2015. His research interest is in social networks and data mining.
