AI RESEARCH
GAD in the Wild: Benchmarking Graph Anomaly Detection under Realistic Deployment Challenges
arXiv CS.AI
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ArXi:2605.07133v1 Announce Type: cross Graph Anomaly Detection (GAD) is a critical task in graph machine learning with vital applications in financial fraud detection and social platform governance. However, existing GAD benchmarks are often restricted to small-scale, curated graphs with relatively balanced anomaly ratios, leaving a substantial gap between academic evaluation and real-world deployment.