38 citations · 190 across the 17 of their papers we have counts for
5 papers · 1 filter
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…