19 citations · 45 across the 8 of their papers we have counts for
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cs.LG2022★ 10 cited
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
Wei Ye, Hao Tian, Qijun Chen
Graph kernels are conventional methods for computing graph similarities. However, the existing R-convolution graph kernels cannot resolve both of the two challenges: 1) Comparing g…
cs.LG2022★ 1 cited
Incorporating Heterophily into Graph Neural Networks for Graph Classification
Jiayi Yang, Sourav Medya, Wei Ye
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily, which means connected nodes tend to have different class labels…
cs.LG2022★ 1 cited
Graph Neural Diffusion Networks for Semi-supervised Learning
Wei Ye, Zexi Huang, Yunqi Hong +1
Graph Convolutional Networks (GCN) is a pioneering model for graph-based semi-supervised learning. However, GCN does not perform well on sparsely-labeled graphs. Its two-layer vers…