Anomaly Detection in Edge Streams
4 papers with code • 1 benchmarks • 1 datasets
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DARPA
Latest papers with no code
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning
In this paper, we propose SLADE (Self-supervised Learning for Anomaly Detection in Edge Streams) for rapid detection of dynamic anomalies in edge streams, without relying on labels.
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations
In this paper, we propose the first approach for outlier detection in edge streams.
