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