SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
J. K. Lee, Sunwoo Kim, Kijung Shin
Abstract
To detect anomalies in real-world graphs, such as social, email, and financial networks, various approaches have been developed. While they typically assume static input graphs, most real-world graphs grow over time, naturally represented as edge streams. In this context, we aim to achieve three goals: (a) instantly detecting anomalies as they occur, (b) adapting to dynamically changing states, and (c) handling the scarcity of dynamic anomaly labels.
Topics & Concepts
Computer scienceEnhanced Data Rates for GSM EvolutionSTREAMSArtificial intelligencePattern recognition (psychology)Computer networkAnomaly Detection Techniques and ApplicationsData Stream Mining TechniquesTime Series Analysis and Forecasting