most citedMeta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

5 citations · 9 across the 4 of their papers we have counts for

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cs.LG2025★ 4 cited

Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks

Yonghao Liu, Mengyu Li, Fausto Giunchiglia +4

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it.Current leading graph model…

cs.LG2024★ 5 cited

Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training

Yonghao Liu, Mengyu Li, Ximing Li +5

Node classification is an essential problem in graph learning. However, many models typically obtain unsatisfactory performance when applied to few-shot scenarios. Some studies hav…