As well as highlighting potentially useful applications for network analysis, this volume identifies new targets for mathematical research that promise to provide insights into network systems theory as well as facilitating the cross-fertilization of ideas between sectors. Focusing on financial, security and social aspects of networking, the volume adds to the growing body of evidence showing that network analysis has applications to transportation, communication, health, finance, and social policy more broadly. It provides powerful models for understanding the behavior of complex systems that, in turn, will impact numerous cutting-edge sectors in science and engineering, such as wireless communication, network security, distributed computing and social networking, financial analysis, and cyber warfare. The volume offers an insider’s view of cutting-edge research in network systems, including methodologies with immense potential for interdisciplinary application. The contributors have all presented material at a series of workshops organized on behalf of Canada’s MITACS initiative, which funds projects and study grants in ‘mathematics for information technology and complex systems’. These proceedings include papers from workshops on financial networks, network security and cryptography, and social networks. MITACS has shown that the partly ghettoized nature of network systems research has led to duplicated work in discrete fields, and thus this initiative has the potential to save time and accelerate the pace of research in a number of areas of network systems research.

First published in 1998, this volume enters the debate on human behaviour in the form of neural networks in a spatial context. As most transportation research techniques had been developed in the 1960s and 1970s, these authors sought to bring that research into the modern era. Featuring 17 articles from 37 contributors, it begins with an overview and proceeds to examine aspects of travel behaviour, traffic flow and traffic management.

Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user’s- behavior, privacy in social network analysis, mobility from spatio-temporal point of view, agent technology and political parties in parliament. These topics will be of interest to researchers and practitioners from different disciplines including, but not limited to, social sciences and engineering.