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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, geo-tagged social media, disease transmission, international relationships and conflict. In many such application settings involving spatiotemporal data, the observed data consist of timestamped relational events. For example, in online 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 structures in those datasets.