Changlong Fu — accepted AI-conference papers

2026

DR-GGAD: Dual Residual Centering for Mitigating Anomaly Non‑Discriminativity in Generalist Graph Anomaly Detection

Changlong Fu, Zhenli He, Xiong Zhang, Cheng Xie, Xin Jin, Yun Yang

ICLR 2026poster

Generalist Graph Anomaly Detection (GGAD) seeks a unified representation learning model to detect anomalies in unseen graphs, but cross-domain transfer often entangles the learned anomalous and normal representations. We formalize this degradation as Anomaly non-Discriminativity (AnD) and define a n…

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2025

IA-GGAD: Zero-shot Generalist Graph Anomaly Detection via Invariant and Affinity Learning

Xiong Zhang, Zhenli He, Changlong Fu, Cheng Xie

NeurIPS 2025spotlight

Generalist Graph Anomaly Detection (GGAD) extends traditional Graph Anomaly Detection (GAD) from one-for-one to one-for-all scenarios, posing significant challenges due to Feature Space Shift (FSS) and Graph Structure Shift (GSS). This paper first formalizes these challenges and proposes quantitativ…

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