  Large volume of networked streaming event data are becoming increasingly
available in a wide variety of applications, such as social network analysis,
Internet traffic monitoring and healthcare analytics. Streaming event data are
discrete observation occurred in continuous time, and the precise time interval
between two events carries a great deal of information about the dynamics of
the underlying systems. How to promptly detect changes in these dynamic systems
using these streaming event data? In this paper, we propose a novel
change-point detection framework for multi-dimensional event data over
networks. We cast the problem into sequential hypothesis test, and derive the
likelihood ratios for point processes, which are computed efficiently via an
EM-like algorithm that is parameter-free and can be computed in a distributed
fashion. We derive a highly accurate theoretical characterization of the
false-alarm-rate, and show that it can achieve weak signal detection by
aggregating local statistics over time and networks. Finally, we demonstrate
the good performance of our algorithm on numerical examples and real-world
datasets from twitter and Memetracker.
