  Can evolving networks be inferred and modeled without directly observing
their nodes and edges? In many applications, the edges of a dynamic network
might not be observed, but one can observe the dynamics of stochastic cascading
processes (e.g., information diffusion, virus propagation) occurring over the
unobserved network. While there have been efforts to infer networks based on
such data, providing a generative probabilistic model that is able to identify
the underlying time-varying network remains an open question. Here we consider
the problem of inferring generative dynamic network models based on network
cascade diffusion data. We propose a novel framework for providing a
non-parametric dynamic network model--based on a mixture of coupled
hierarchical Dirichlet processes-- based on data capturing cascade node
infection times. Our approach allows us to infer the evolving community
structure in networks and to obtain an explicit predictive distribution over
the edges of the underlying network--including those that were not involved in
transmission of any cascade, or are likely to appear in the future. We show the
effectiveness of our approach using extensive experiments on synthetic as well
as real-world networks.
