Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification-猫眼课题宝

DOI：

10.48550/arxiv.2212.05606

发表时间：

2022-12

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Zhen Tan;Song Wang;Kaize Ding;Jundong Li;Huan Liu

少镜头节点分类的任务是提供准确的预测节点从新的类只有少数代表性的标记节点。这个问题已经引起了极大的关注，因为它的投影到流行的现实世界的应用，如产品分类的新添加的商品类别的电子商务平台上的稀缺记录或罕见疾病的诊断上的患者相似性图。为了解决非欧几里德图域中具有挑战性的标签稀缺问题，元学习已经成为一种成功和主导的范式。最近，受图自监督学习发展的启发，为少数节点分类转移预训练的节点嵌入可能是元学习的一个有前途的替代方案，但尚未公开。在这项工作中，我们经验性地展示了另一种框架的潜力，\textit{Transductive Linear Probing}，它可以传输从图对比学习方法中学习的预训练节点嵌入。我们进一步将少拍节点分类的设置从标准的完全监督扩展到更现实的自监督设置，其中由于缺乏训练类的监督，元学习方法无法轻松部署。令人惊讶的是，即使没有任何地面事实标签，具有自监督图对比预训练的直推线性探测也可以在相同协议下优于最先进的完全监督元学习方法。我们希望这项工作可以为少数节点分类问题提供新的思路，并促进未来从图上几乎没有标记的实例中学习的研究。

Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an E-commerce platform with scarce records or diagnoses for rare diseases on a patient similarity graph. To tackle such challenging label scarcity issues in the non-Euclidean graph domain, meta-learning has become a successful and predominant paradigm. More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed. In this work, we empirically demonstrate the potential of an alternative framework, \textit{Transductive Linear Probing}, that transfers pretrained node embeddings, which are learned from graph contrastive learning methods. We further extend the setting of few-shot node classification from standard fully supervised to a more realistic self-supervised setting, where meta-learning methods cannot be easily deployed due to the shortage of supervision from training classes. Surprisingly, even without any ground-truth labels, transductive linear probing with self-supervised graph contrastive pretraining can outperform the state-of-the-art fully supervised meta-learning based methods under the same protocol. We hope this work can shed new light on few-shot node classification problems and foster future research on learning from scarcely labeled instances on graphs.

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

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