Subset Node Anomaly Tracking over Large Dynamic Graphs
Xingzhi Guo, Baojian Zhou, Steven Skiena
2022Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining21 citationsDOIOpen Access PDF
Abstract
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.
Topics & Concepts
Computer scienceAnomaly detectionAnomaly (physics)Node (physics)Tracking (education)Artificial intelligencePhysicsPsychologyCondensed matter physicsPedagogyQuantum mechanicsComplex Network Analysis TechniquesData Stream Mining TechniquesNetwork Security and Intrusion Detection