62 citations · 168 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Muyang Li +2
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on…
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Sixiao Zhang +4
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-qu…
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…