Parameter Learning for Latent Network Diffusion

Diffusion processes in networks are increasingly used to model dynamic phenomena such as the spread of information, wildlife, or social influence. Our work addresses the problem of learning the un-derlying parameters that govern such a diffusion process by observing the time at which nodes be-come active. A key advantage of our approach is that, unlike previous work, it can tolerate missing observations for some nodes in the diffusion pro-cess. Having incomplete observations is character-istic of offline networks used to model the spread of wildlife. We develop an EM algorithm to address parameter learning in such settings. Since both the E and M steps are computationally challenging, we employ a number of optimization methods such as nonlinear and difference-of-convex programming to address these challenges. Evaluation of the ap-proach on the Red-cockaded Woodpecker conser-vation problem shows that it is highly robust and accurately learns parameters in various settings, even with more than 80 % missing data.