Abstract: Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task ...

then you can perform global matching (e.g., cosine similarity) or local matching (e.g., L2 loss) to obtain the matching results (referred to as the cost volume in this paper). These results can be ...

This repository provides reproducible implementation of the anomaly detection method based on a denoising autoencoder architecture with diffusion noise scheduling mechanism inspired by diffusion ...

Abstract: Critical infrastructure (CI) is essential for societal and economic stability, making it a prime target for cyber threats. Traditional anomaly detection models like LSTM and Transformers ...