7 citations · 13 across the 5 of their papers we have counts for
4 papers · 1 filter
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju +2
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most exist…
Graph Contrastive Learning with Cross-view Reconstruction
Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang +3
Among different existing graph self-supervised learning strategies, graph contrastive learning (GCL) has been one of the most prevalent approaches to this problem. Despite the rema…
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen +4
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capabilit…
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Chunhui Zhang, Chao Huang, Yijun Tian +5
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs,…