6 papers · 1 filter
Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
Yumeng Wang, Zengyi Wo, Wenjun Wang +2
Graph Neural Networks (GNNs) excel in node classification tasks but often assume homophily, where connected nodes share similar labels. This assumption does not hold in many real-w…
Addressing Graph Anomaly Detection via Causal Edge Separation and Spectrum
Zengyi Wo, Wenjun Wang, Minglai Shao +3
In the real world, anomalous entities often add more legitimate connections while hiding direct links with other anomalous entities, leading to heterophilic structures in anomalous…
Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Zengyi Wo, Chang Liu, Yumeng Wang +2
Graph Neural Networks (GNNs) have been successful in modeling graph-structured data. However, similar to other machine learning models, GNNs can exhibit bias in predictions based o…
MLDGG: Meta-Learning for Domain Generalization on Graphs
Qin Tian, Chen Zhao, Minglai Shao +3
Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testin…
Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
Dong Li, Chen Zhao, Minglai Shao +1
Achieving the generalization of an invariant classifier from training domains to shifted test domains while simultaneously considering model fairness is a substantial and complex c…
Graphs Generalization under Distribution Shifts
Qin Tian, Wenjun Wang, Chen Zhao +3
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the…