图联邦元学习进行少样本图分类的论文推荐

以下是几篇推荐的论文：

1. "Meta-Learning for Few-Shot Graph Classification" by Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande and Jure Leskovec.

2. "Few-shot Learning on Graphs via Super-classes based on Graph Spectral Measures" by Yaqing Wang, Han Liu, Jing Gao, and Wei Fan.

3. "Graph Few-shot Learning via Knowledge Transfer" by Shengding Hu, Xiaorui Liu, Zhihua Zhang, and Jianping Yin.

4. "Graph Few-shot Learning with Relation-Specific Attention Networks" by Liang Yang, Xiaodong He, Jianfeng Gao, and Jingrui He.

5. "Few-shot Graph Classification via Adapted Graph Convolutional Networks" by Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu.

这些论文都探讨了如何使用联邦元学习进行少样本图分类。它们使用了不同的技术来实现这个目标，例如基于图谱测量的超类、知识转移、关系特定的注意力网络、自适应图卷积网络等。这些论文的实验结果表明，联邦元学习可以有效地解决少样本图分类的问题