Sequential change-point detection for a network of Hawkes processes

Yao Xie

Hawkes processes has been a popular point process model for capturing mutual excitation of discrete events. In the network setting, this can capture the mutual influence between nodes, which has a wide range of applications in neural science, social networks, and crime data analysis. In this talk, I will present a statistical change-point detection framework to detect in real-time, a change in the influence using streaming discrete events. Theoretical results are provided for controlling false alarms, characterizing the trade-off between the average-run-length and the expected detection delay, as well proving an online estimation procedure is nearly optimal.

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