Continuous-time probabilistic generative models for dynamic networks
Data Science Seminar
Kevin Xu (Case Western Reserve University)
Networks are ubiquitous in science, serving as a natural representation for many complex physical, biological, and social systems. Probabilistic generative models for networks provide plausible mechanisms by which network data are generated to reveal insights about the underlying complex system. Such complex systems are often time-varying, which has led to the development of dynamic network representations to enable modeling, analysis, and prediction of temporal dynamics.
In this talk, I introduce a class of continuous-time probabilistic generative models for dynamic networks that augment statistical models for network structure with multivariate Hawkes processes to model temporal dynamics. The class of models allows an analyst to trade off flexibility and scalability of a model depending on the application setting. I focus on two specific models on opposite ends of the tradeoff: the community Hawkes independent pairs (CHIP) model that scales up to millions of nodes, and the multivariate Community Hawkes (MULCH) model that is flexible enough to replicate a variety of observed structures in real network data, including temporal motifs. I demonstrate how these models can be used for analysis, prediction, and simulation on several real network data sets, including a network of militarized disputes between countries over time.