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cs.LG2023
Contrastive Meta-Learning for Few-shot Node Classification
Song Wang, Zhen Tan, Huan Liu +1
Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining ta…
cs.LG2023
Inductive Linear Probing for Few-shot Node Classification
Hirthik Mathavan, Zhen Tan, Nivedh Mudiam +1
Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing liter…
cs.LG2023★ 2 cited
Virtual Node Tuning for Few-shot Node Classification
Zhen Tan, Ruocheng Guo, Kaize Ding +1
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-…