Applications such as neuroscience, telecommunication, online social networking,\ud transport and retail trading give rise to connectivity patterns that change over time.\ud In this work, we address the resulting need for network models and computational\ud algorithms that deal with dynamic links. We introduce a new class of evolving\ud range-dependent random graphs that gives a tractable framework for modelling and\ud simulation. We develop a spectral algorithm for calibrating a set of edge ranges from\ud a sequence of network snapshots and give a proof of principle illustration on some\ud neuroscience data. We also show how the model can be used computationally and\ud analytically to investigate the scenario where an evolutionary process, such as an\ud epidemic, takes place on an evolving network. This allows us to study the cumulative\ud effect of two distinct types of dynamics
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