5 citations · 9 across the 3 of their papers we have counts for
3 papers
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.CL2025
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Yonghao Liu, Mengyu Li, Wei Pang +4
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical sce…
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…