30 citations · 66 across the 12 of their papers we have counts for
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cs.LG2023
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
Tianrui Jia, Haoyang Li, Cheng Yang +2
Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking in generalization capability when tackling out-of-distribut…
cs.LG2023★ 30 cited
Graph Foundation Models: Concepts, Opportunities and Challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu +8
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and seve…
cs.LG2023★ 4 cited
Data-centric Graph Learning: A Survey
Yuxin Guo, Deyu Bo, Cheng Yang +5
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Rece…