42 citations · 85 across the 20 of their papers we have counts for
4 papers · 1 filter
Graph Transformers for Large Graphs
Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu +3
Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, thes…
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
Yu Wang, Tong Zhao, Yuying Zhao +4
Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs…
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
Mingxuan Ju, Tong Zhao, Wenhao Yu +2
Recent studies have shown that graph neural networks (GNNs) exhibit strong biases towards the node degree: they usually perform satisfactorily on high-degree nodes with rich neighb…
Revisiting Link Prediction: A Data Perspective
Haitao Mao, Juanhui Li, Harry Shomer +6
Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction. How…