ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph

Given electrical sensors placed on the power grid, how can we automatically determine when electrical components (e.g. power lines) fail? Or, given traffic sensors which measure the speed of vehicles passing over them, how can we determine when traffic accidents occur? Both these problems involve detecting change points in a set of sensors on the nodes or edges of a graph. To this end, we propose ChangeDAR (Change Detection And Resolution), which detects changes in an online manner, and reports when and where the change occurred in the graph. Our contributions are: 1) Algorithm : we propose novel information-theoretic optimization objectives for scoring and detecting localized changes, and propose two algorithms, ChangeDAR-S and ChangeDAR-D respectively, to optimize them. 2) Theoretical Guarantees : we show that both methods provide constant-factor approximation guarantees (Theorems 5.2 and 6.2). 3) Effectiveness : in experiments, ChangeDAR detects traffic accidents and power line failures with 75% higher F-measure than comparable baselines. 4) Scalability : ChangeDAR is online and near-linear in the graph size and the number of time ticks.