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.
In Proc. of NIPS'18, the 32nd Annual Conference on Neural Information Processing Systems, Montreal, Canada, December 2018.
*This work was partially supported by the NSF-Frontiers Cyber-Cardia
Award, the US-AFOSR Arrive Award, the EU-Artemis EMC2 Award, the
EU-Ecsel iDev Award, EU-Ecsel Semi40 Award, the EU-Ecsel Productive 4.0 Award, the
AT-FWF-NFN RiSE Award, the AT-FWF-LogicCS-DC Award, the AT-FFG
Harmonia Award, the AT-FFG Em2Apps Award, and the TUW-CPPS-DK Award.