  Continuous time temporal networks are attracting increasing attention due
their omnipresence in real-world datasets and they manifold applications. While
static network models have been successful in capturing static topological
regularities, they often fail to model effects coming from the causal nature
that explain the generation of networks. Exploiting the temporal aspect of
networks has thus been the focus of various studies in the last decades.
  We propose a framework for designing generative models for continuous time
temporal networks. Assuming a first order Markov assumption on the
edge-specific temporal point processes enables us to flexibly apply survival
analysis models directly on the waiting time between events, while using
time-varying history-based features as covariates for these predictions. This
approach links the well-documented field of temporal networks analysis through
multivariate point processes, with methodological tools adapted from survival
analysis. We propose a fitting method for models within this framework, and an
algorithm for simulating new temporal networks having desired properties. We
evaluate our method on a downstream future link prediction task, and provide a
qualitative assessment of the network simulations.
