Network science offers a powerful language to represent and study complex systems composed of interacting elements - from the Internet to social and biological systems. A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks and provides connections between the different areas of knowledge required to address this multi-disciplinary subject. After an introduction to key concepts on networks and stochastic dynamics, the authors guide the reader through a coherent selection of mathematical and computational tools for network dynamics. Perfect for students and professionals, this book is a gateway to an active field of research developing between the disciplines of applied mathematics, physics and computer science, with applications in others including social sciences, neuroscience and biology.

This second edition extensively expands upon the coverage of the first edition as the authors expertly present recent theoretical and modelling progress in the emerging field of temporal networks, providing the keys to (and connections between) the different areas of knowledge required to address this multi-disciplinary problem.

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Naoki Masuda received his PhD in 1998 from the University of Tokyo. He has been a lecturer and professor at the University of Tokyo, Japan, and the University of Bristol, UK, and since 2019 has been Associate Professor at the Department of Mathematics, State University of New York at Buffalo. He is an editorial board member of the Journal of Complex Networks, Scientific Reports, PLOS ONE, and other journals. His main research interests are network science and mathematical biology.

Renaud Lambiotte has a PhD in physics from the Universit� libre de Bruxelles. After completing postdocs at CECAM/ENS Lyon, Universit� de Li�ge, UCLouvain and Imperial College London, as well as a professorship in Mathematics at the University of Namur, he became associate professor at the Mathematical Institute of Oxford University. His main research interests are the modelling and analysis of processes on large networks, with a particular focus on social and brain networks.

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