Point Process Models on Networks

• Activity Number: 214
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Andrea Bertozzi*+
• Companies: University of California, Los Angeles

Abstract

We discuss a mathematical framework based on a self-exciting point process aimed at analyzing temporal patterns in the series of interaction events between agents in a social network. We show how such models can apply to a variety of real-world datasets such as gang crimes and email traffic. We discuss reconstruction models that allows one to predict the unknown participants in a portion of those events when only partial information is available. Such approaches can be useful for solving unsolved gang crimes or for security applications involving cyber communication.