Network science offers a powerful language to represent and study complex systems composed of interacting elements — from the Internet to social and biological systems. In its standard formulation, this framework relies on the assumption that the underlying topology is static, or changing very slowly as compared to dynamical processes taking place on it, e.g., epidemic spreading or navigation. Fuelled by the increasing availability of longitudinal networked data, recent empirical observations have shown that this assumption is not valid in a variety of situations. Instead, often the network itself presents rich temporal properties and new tools are required to properly describe and analyse their behaviour.
A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks, and provides connections between 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.
Network science offers a powerful language to represent and study complex systems composed of interacting elements — from the Internet to social and biological systems. In its standard formulation, this framework relies on the assumption that the underlying topology is static, or changing very slowly as compared to dynamical processes taking place on it, e.g., epidemic spreading or navigation. Fuelled by the increasing availability of longitudinal networked data, recent empirical observations have shown that this assumption is not valid in a variety of situations. Instead, often the network itself presents rich temporal properties and new tools are required to properly describe and analyse their behaviour.
A Guide to Temporal Networks presents recent theoretical and modelling progress in the emerging field of temporally varying networks, and provides connections between 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.
