webinar register page

IMA Data Science Seminar - Kevin Xu
Speaker: Kevin Xu
Title: Continuous-time probabilistic generative models for
dynamic networks
Abstract: 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.

Apr 4, 2023 01:25 PM in Central Time (US and Canada)

* Required information


Kevin Xu
Assistant Professor @Case Western Reserve University
Kevin S. Xu received the B.A.Sc. degree from the University of Waterloo in 2007 and the M.S.E. and Ph.D. degrees from the University of Michigan in 2009 and 2012, respectively. He is a recipient of the NSF CAREER award, and his research has been supported by several NSF and NIH grants. He is currently an assistant professor in the Department of Computer and Data Sciences at Case Western Reserve University. He was previously an assistant professor at the University of Toledo and held industry research positions at Technicolor and 3M. His main research interests are in machine learning and network science with applications to human dynamics, health care, education, and wearable computing.