519 citations · 1.4k across the 25 of their papers we have counts for
25 papers
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
Xiao Wang, Nian Liu, Hui Han +1
Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follo…
Lorentzian Graph Convolutional Networks
Yiding Zhang, Xiao Wang, Chuan Shi +2
Graph convolutional networks (GCNs) have received considerable research attention recently. Most GCNs learn the node representations in Euclidean geometry, but that could have a hi…
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework
Cheng Yang, Jiawei Liu, Chuan Shi
Semi-supervised learning on graphs is an important problem in the machine learning area. In recent years, state-of-the-art classification methods based on graph neural networks (GN…
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Meiqi Zhu, Xiao Wang, Chuan Shi +2
Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks. The well-designed propagation mechanism which has b…
Beyond Low-frequency Information in Graph Convolutional Networks
Deyu Bo, Xiao Wang, Chuan Shi +1
Graph neural networks (GNNs) have been proven to be effective in various network-related tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which…
Learning Node Representations from Noisy Graph Structures
Junshan Wang, Ziyao Li, Qingqing Long +3
Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks…