38 citations · 190 across the 17 of their papers we have counts for
18 papers
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
Zhongjian Zhang, Yue Yu, Mengmei Zhang +3
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a widely recognized paradigm, Gra…
Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework
Yujie Xing, Xiao Wang, Bin Wu +2
Graph Transformers (GTs) have emerged as a powerful paradigm for graph representation learning due to their ability to model diverse node interactions. However, existing GTs often…
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…
Minimum Topology Attacks for Graph Neural Networks
Mengmei Zhang, Xiao Wang, Chuan Shi +3
With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have be…
Graph Fairness Learning under Distribution Shifts
Yibo Li, Xiao Wang, Yujie Xing +4
Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory predi…
Graph Contrastive Invariant Learning from the Causal Perspective
Yanhu Mo, Xiao Wang, Shaohua Fan +1
Graph contrastive learning (GCL), learning the node representation by contrasting two augmented graphs in a self-supervised way, has attracted considerable attention. GCL is usuall…
A Generalized Neural Diffusion Framework on Graphs
Yibo Li, Xiao Wang, Hongrui Liu +1
Recent studies reveal the connection between GNNs and the diffusion process, which motivates many diffusion-based GNNs to be proposed. However, since these two mechanisms are close…
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…
A Post-Training Framework for Improving Heterogeneous Graph Neural Networks
Cheng Yang, Xumeng Gong, Chuan Shi +1
Recent years have witnessed the success of heterogeneous graph neural networks (HGNNs) in modeling heterogeneous information networks (HINs). In this paper, we focus on the benchma…
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…
A Survey on Spectral Graph Neural Networks
Deyu Bo, Xiao Wang, Yang Liu +3
Graph neural networks (GNNs) have attracted considerable attention from the research community. It is well established that GNNs are usually roughly divided into spatial and spectr…
Directed Acyclic Graph Structure Learning from Dynamic Graphs
Shaohua Fan, Shuyang Zhang, Xiao Wang +1
Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights…
Uncovering the Structural Fairness in Graph Contrastive Learning
Ruijia Wang, Xiao Wang, Chuan Shi +1
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed deg…
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…
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
Shaohua Fan, Xiao Wang, Yanhu Mo +2
Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification…
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Tianyu Zhao, Cheng Yang, Yibo Li +7
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due…
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…
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…