358 citations · 669 across the 35 of their papers we have counts for
10 papers · 1 filter
TopoImb: Toward Topology-level Imbalance in Learning from Graphs
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang +1
Graph serves as a powerful tool for modeling data that has an underlying structure in non-Euclidean space, by encoding relations as edges and entities as nodes. Despite development…
HP-GMN: Graph Memory Networks for Heterophilous Graphs
Junjie Xu, Enyan Dai, Xiang Zhang +1
Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption…
Towards Prototype-Based Self-Explainable Graph Neural Network
Enyan Dai, Suhang Wang
Graph Neural Networks (GNNs) have shown great ability in modeling graph-structured data for various domains. However, GNNs are known as black-box models that lack interpretability.…
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Shijie Zhou, Zhimeng Guo, Charu Aggarwal +2
Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homo…
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks
Tianxiang Zhao, Xiang Zhang, Suhang Wang
In recent years, graph neural networks (GNNs) have achieved state-of-the-art performance for node classification. However, most existing GNNs would suffer from the graph imbalance…
Decoupled Self-supervised Learning for Non-Homophilous Graphs
Teng Xiao, Zhengyu Chen, Zhimeng Guo +2
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is…