The authors built DynGraphAgentBench around seven temporal graph datasets covering node-level and edge-level anomaly detection.
Benchmark tests agent control of anomaly detectors under delayed feedback
DynGraphAgentBench evaluates whether controllers can select and adapt graph anomaly detectors without current-window labels, scores, or exhaustive model training.
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Yuwei Han · Lingwei Wei · Wooseong Yang · Liangjie Huang · Liancheng Fang · Huanhuan Ma · +1 more
Research Digest··2 min read
Han et al.
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DynGraphAgentBench evaluates anomaly detection controllers that must select detectors under delayed feedback across temporal graph datasets.
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