91 citations · 198 across the 8 of their papers we have counts for
13 papers · 1 filter
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…
Elastic Graph Neural Networks
Xiaorui Liu, Wei Jin, Yao Ma +5
While many existing graph neural networks (GNNs) have been proven to perform -based graph smoothing that enforces smoothness globally, in this work we aim to further enhanc…
Graph Feature Gating Networks
Wei Jin, Xiaorui Liu, Yao Ma +3
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where…
Node Similarity Preserving Graph Convolutional Networks
Wei Jin, Tyler Derr, Yiqi Wang +3
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world applications due to their strong ability in graph representation learning. GNNs explore the grap…