272 citations · 621 across the 17 of their papers we have counts for
5 papers · 1 filter
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
Xingcheng Fu, Yuecen Wei, Qingyun Sun +4
Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topo…
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Qingyun Sun, Jianxin Li, Haonan Yuan +5
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs. What topolog…
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu, Youzhi Luo, Limei Wang +13
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementin…
Deep Learning of High-Order Interactions for Protein Interface Prediction
Yi Liu, Hao Yuan, Lei Cai +1
Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface fro…
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Hao Yuan, Jiliang Tang, Xia Hu +1
Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information, which have achieved promising performance on many graph tasks. However, GNNs ar…