27 citations · 58 across the 9 of their papers we have counts for
8 papers · 1 filter
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
Donglin Xia, Xiao Wang, Nian Liu +1
Graph neural networks (GNNs) have become increasingly popular in modeling graph-structured data due to their ability to learn node representations by aggregating local structure in…
Provable Training for Graph Contrastive Learning
Yue Yu, Xiao Wang, Mengmei Zhang +2
Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximi…
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network
Nian Liu, Xiao Wang, 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…
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Nian Liu, Xiao Wang, Deyu Bo +2
Graph Contrastive Learning (GCL), learning the node representations by augmenting graphs, has attracted considerable attentions. Despite the proliferation of various graph augmenta…
Debiased Graph Neural Networks with Agnostic Label Selection Bias
Shaohua Fan, Xiao Wang, Chuan Shi +3
Most existing Graph Neural Networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with test set.…
Compact Graph Structure Learning via Mutual Information Compression
Nian Liu, Xiao Wang, Lingfei Wu +3
Graph Structure Learning (GSL) recently has attracted considerable attentions in its capacity of optimizing graph structure as well as learning suitable parameters of Graph Neural…
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…