91 citations · 572 across the 26 of their papers we have counts for
28 papers
Towards Fair Graph Neural Networks via Graph Counterfactual
Zhimeng Guo, Jialiang Li, Teng Xiao +2
Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent w…
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Juanhui Li, Harry Shomer, Haitao Mao +5
Link prediction attempts to predict whether an unseen edge exists based on only a portion of edges of a graph. A flurry of methods have been introduced in recent years that attempt…
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Haitao Mao, Zhikai Chen, Wei Jin +5
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and…
Learning Representations for Hyper-Relational Knowledge Graphs
Harry Shomer, Wei Jin, Juanhui Li +2
Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts…
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
Wei Jin, Xiaorui Liu, Yao Ma +2
Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the succe…
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
Juanhui Li, Harry Shomer, Jiayuan Ding +5
Knowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completio…