19 citations · 45 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Deep Embedded K-Means Clustering
Wengang Guo, Kaiyan Lin, Wei Ye
Recently, deep clustering methods have gained momentum because of the high representational power of deep neural networks (DNNs) such as autoencoder. The key idea is that represent…
Connecting Latent ReLationships over Heterogeneous Attributed Network for Recommendation
Ziheng Duan, Yueyang Wang, Weihao Ye +3
Recently, deep neural network models for graph-structured data have been demonstrating to be influential in recommendation systems. Graph Neural Network (GNN), which can generate h…
Learning Deep Graph Representations via Convolutional Neural Networks
Wei Ye, Omid Askarisichani, Alex Jones +1
Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. R-convolution graph kernels are pos…