AIBearisharXiv – CS AI · 9h ago7/10
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GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges
Researchers have published a comprehensive benchmark for Graph Anomaly Detection (GAD) models that exposes critical gaps between academic performance and real-world deployment. The study reveals that leading GAD methods fail to scale to million-node graphs, collapse under realistic anomaly scarcity (0.1%), and struggle with missing data—challenges absent from typical laboratory benchmarks.