5 papers
Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability
Jialiang Yin, Zheng Zhao, Linsey Pang +3
Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in high-stakes domains. However,…
Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
Jiaxin Zhang, Yiqi Wang, Siwei Wang +4
Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, e…
Mixture of Experts for Node Classification
Yu Shi, Yiqi Wang, WeiXuan Lang +3
Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of no…
Test-Time Training on Graphs with Large Language Models (LLMs)
Jiaxin Zhang, Yiqi Wang, Xihong Yang +6
Graph Neural Networks have demonstrated great success in various fields of multimedia. However, the distribution shift between the training and test data challenges the effectivene…
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Jingcan Duan, Pei Zhang, Siwei Wang +5
Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to impro…