1 citations · 1 across the 2 of their papers we have counts for
4 papers
A Unified Invariant Learning Framework for Graph Classification
Yongduo Sui, Jie Sun, Shuyao Wang +4
Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize sta…
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
Jinghan Li, Yuan Gao, Jinda Lu +4
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods,…
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Junfeng Fang, Xinglin Li, Yongduo Sui +5
Graph representation learning on vast datasets, like web data, has made significant strides. However, the associated computational and storage overheads raise concerns. In sight of…
MolTC: Towards Molecular Relational Modeling In Language Models
Junfeng Fang, Shuai Zhang, Chang Wu +6
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research. Recently, the adoption of la…