This book focuses on the mathematical theories and algorithms for information processing and network inference in complex engineered networks such as the Internet and online social networks. These large-scale networks provide an important and diverse medium for spreading and disseminating various types of information. The spreading processes are those in which the actions (e.g. computer virus threat, rumour spreading, viral marketing) by certain nodes increase the susceptibility of other nodes to do likewise; this results in cascading phenomena from a small set of initial nodes to a much larger set.

The book presents mathematical tools based on statistical inference, maximum likelihood estimation and graph theory to help readers understand these complex network dynamics and their problems. It not only introduces and explains how to design data analytics and reliable network forensics to tackle cyber security problems such as the discovery of cyber threat source, but also presents insights into forward-engineering new applications such as viral marketing and the design of future complex networks. As such it is a valuable resource for graduate students and advanced researchers in the field of information processing and network inference

Provides insights into essential theories and algorithms for information processing and network inference in complex engineered networks

Equips readers to handle complex algorithm design based on statistical inference and maximum-likelihood estimation for reliable network forensics

Includes new cyber security protocols and their software implementation in practical complex engineered networks such as online social networks, computer networks, and smart grids

Written by a leading expert in the field

Autorentext

Chee Wei Tan received the B.S. (First Class Honors) in electrical engineering from the National University of Singapore in 2002, and the M.A. and Ph.D. degrees in electrical engineering from Princeton University, Princeton, NJ, in 2006 and 2008, respectively. He is an Assistant Professor at the City University Hong Kong. He was a Postdoctoral Scholar at the California Institute of Technology, and affiliated with the Netlab and the Rigorous Systems Research Group at Caltech. He is currently serving as an Editor for the IEEE Transactions on Communications (TCOM) and the Chair of the IEEE Information Theory Society Hong Kong Chapter. Dr. Tan was the recipient of the 2008 Princeton University Wu Prize for Excellence, a 2011 IEEE Communications Society Asia-Pacific Young Researcher Award and a selected participant of the US. National Academy of Engineering 2013 China-America Frontiers of Engineering Symposium.

Inhalt
Introduction to Network Statistical Inference.- Spreading Models in Online Social Networks.- Mathematics of Spreading in Networks.- Single Rumor Source and Multiple Sources Detection Problems.- Computational Algorithms for Rumor Source Detection.- Advanced Topics in Network Inference Problems.- Cyber-Security Protocol Design and Network Forensics Implementation.