Subset Node Anomaly Tracking over Large Dynamic Graphs

Tracking a targeted subset of nodes in an evolving graph is important for many real-world applications. Existing methods typically focus on identifying anomalous edges or finding anomaly graph snapshots in a stream way. However, edge-oriented methods cannot quantify how individual nodes change over time while others need to maintain representations of the whole graph all the time, thus computationally inefficient. This paper proposes DynAnom, an efficient framework to quantify the changes and localize per-node anomalies over large dynamic weighted-graphs. Thanks to recent advances in dynamic representation learning based on Personalized PageRank, DynAnom is 1) efficient: the time complexity is linear to the number of edge events and independent of node size of the input graph; 2) effective: Dy-nAnom can successfully track topological changes reflecting realworld anomaly; 3) flexible: different type of anomaly score functions can be defined for various applications. Experiments demonstrate these properties on both benchmark graph datasets and a new large real-world dynamic graph. Specifically, an instantiation method based on DynAnom achieves the accuracy of 0.5425 compared with 0.2790, the best baseline, on the task of node-level anomaly localization while running 2.3 times faster than the baseline. We present a real-world case study and further demonstrate the usability of DynAnom for anomaly discovery over large-scale graphs.