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arXiv Machine Learning By Yujing Liu, Yixin Liu, Yue Tan, Xiaofeng Cao, Alan Wee-Chung Liew, Heng Tao Shen, Shirui Pan

Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection?

Read the original on arXiv Machine Learning →

The paper investigates whether synthetic data can replace real-world training data for generalist graph anomaly detection (GAD). It introduces AG-FORGE, a framework that automatically generates anomalous graphs, and shows that synthetic data can match real-world performance but does not surpass it due to method limitations. To overcome this, the authors propose TS-GGAD, a model that jointly learns topological and semantic anomaly cues with a curriculum learning strategy, achieving superior results on 14 real-world datasets when trained on AG-FORGE-generated data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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