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Continuous time temporal networks are attracting increasing attention duetheir omnipresence in real-world datasets and they manifold applications. Whilestatic network models have been successful in capturing static topologicalregularities, they often fail to model effects coming from the causal naturethat explain the generation of networks. Exploiting the temporal aspect ofnetworks has thus been the focus of various studies in the last decades.  We propose a framework for designing generative models for continuous timetemporal networks. Assuming a first order Markov assumption on theedge-specific temporal point processes enables us to flexibly apply survivalanalysis models directly on the waiting time between events, while usingtime-varying history-based features as covariates for these predictions. Thisapproach links the well-documented field of temporal networks analysis throughmultivariate point processes, with methodological tools adapted from survivalanalysis. We propose a fitting method for models within this framework, and analgorithm for simulating new temporal networks having desired properties. Weevaluate our method on a downstream future link prediction task, and provide aqualitative assessment of the network simulations.

Graph-Survival: A Survival Analysis Framework for Machine Learning on Temporal Networks