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.
A Guide to Temporal Networks (Series on Complexity Science) read online free book
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