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cs.LG2026
TA-GGAD: Testing-time Adaptive Graph Model for Generalist Graph Anomaly Detection
Xiong Zhang, Hong Peng, Changlong Fu +3
A significant number of anomalous nodes in the real world, such as fake news, noncompliant users, malicious transactions, and malicious posts, severely compromises the health of th…
cs.LG2025★ 1 cited
JTreeformer: Graph-Transformer via Latent-Diffusion Model for Molecular Generation
Ji Shi, Chengxun Xie, Zhonghao Li +2
The discovery of new molecules based on the original chemical molecule distributions is of great importance in medicine. The graph transformer, with its advantages of high performa…
cs.LG2024
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
Xiong Zhang, Cheng Xie, Haoran Duan +1
Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream lear…