A0751
Title: Modeling continuous-time networks of relational events
Authors: Subhadeep Paul - The Ohio State University (United States) [presenting]
Abstract: Spatiotemporal data with complex network dependencies are increasingly available in many application problems involving human mobility, social media, disease transmission, and international relationships. The observed data consist of timestamped relational events in many such application settings. For example, in social media, users interact with each other through events that occur at specific time instances such as liking, mentioning, commenting, or sharing another user's content. In international relations and conflicts, nations commit acts of hostility or disputes through discrete time-stamped events. We will introduce statistical models and methods for analyzing such datasets combining tools from network analysis and multivariate point processes. We will also describe scalable estimation methods and study the asymptotic properties of the estimators. Finally, we will demonstrate the models are able to fit several real datasets well and predict temporal motif structures in those datasets.
