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Full Description
This book provides an overview of network science from the perspective of diverse academic fields, offering insights into the various research areas within network science. The authoritative contributions on statistical network analysis, mathematical network science, genetic networks, Bayesian networks, network visualisation, and systemic risk in networks explore the main questions in the respective fields: What has been achieved to date? What are the research challenges and obstacles? What are the possible interconnections with other fields? And how can cross-fertilization between these fields be promoted?
Network science comprises numerous scientific disciplines, including computer science, economics, mathematics, statistics, social sciences, bioinformatics, and medicine, among many others. These diverse research areas require and use different data-analytic and numerical methods as well as different theoretical approaches. Nevertheless, they all examine and describe interdependencies, associations, and relationships of entities in different kinds of networks.
The book is intended for researchers as well as interested readers working in network science who want to learn more about the field - beyond their own research or work niche. Presenting network science from different perspectives without going into too much technical detail, it allows readers to gain an overview without having to be a specialist in any or all of these disciplines.
Contents
Preface.- Introduction (Francesca Biagini, Göran Kauermann, Thilo Meyer-Brandis).- Network Visualization (Ulrik Brandes and Michael Sedlmair).- A Statistician's View of Network Modeling (David R. Hunter).- The Rank-one and the Preferential Attachment Paradigm (Steffen Dereich).- Systemic Risk in Networks (Nils Detering, Thilo Meyer-Brandis, Konstantinos Panagiotou, Daniel Ritter).- Bayesian Networks for Max-linear Models (Claudia Klüppelberg, Steffen Lauritzen).- Introduction to Network Inference in Genomics (Ernst C. Wit).
