Detecting weak changes in dynamic events over networks
This addresses the need for timely change detection in applications such as social network analysis and healthcare analytics, representing a novel method for a known bottleneck rather than an incremental improvement.
The paper tackles the problem of promptly detecting weak changes in dynamic networked systems using streaming event data, proposing a novel change-point detection framework that achieves accurate false-alarm-rate characterization and demonstrates good performance on real-world datasets like Twitter and Memetracker.
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.