519 citations · 1.4k across the 30 of their papers we have counts for
6 papers · 1 filter
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Shaohua Fan, Xiao Wang, Chuan Shi +2
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization…
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration
Xiao Wang, Hongrui Liu, Chuan Shi +1
Despite Graph Neural Networks (GNNs) have achieved remarkable accuracy, whether the results are trustworthy is still unexplored. Previous studies suggest that many modern neural ne…
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…
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…