Awesome Deep Graph Anomaly Detection
A comprehensive research resource for deep graph anomaly detection, featuring representative papers, datasets, methods, tutorials, and related materials.
Open-source research
GitHub Projects
Awesome Anomaly Detection Foundation Models
A curated collection of recent foundation-model research for anomaly detection across tabular data, time series, images, videos, graphs, text, and logs.
VerifyMAS
Hypothesis verification for failure attribution in LLM-based multi-agent systems, supporting error identification and responsible-agent localization from complete execution trajectories.