58 citations · 69 across the 4 of their papers we have counts for
4 papers
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Sirui Huang +2
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learnin…
Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks
Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu +1
The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant i…
Dual Space Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Shirui Pan +3
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive l…
Graph Masked Autoencoders with Transformers
Sixiao Zhang, Hongxu Chen, Haoran Yang +3
Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenari…