Title: Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph Show Chinese title

Title: Hound：文本属性图上少样本和零样本节点分类的狩猎监督信号

Abstract: Text-attributed graph (TAG) is an important type of graph structured data with text descriptions for each node. Few- and zero-shot node classification on TAGs have many applications in fields such as academia and social networks. However, the two tasks are challenging due to the lack of supervision signals, and existing methods only use the contrastive loss to align graph-based node embedding and language-based text embedding. In this paper, we propose Hound to improve accuracy by introducing more supervision signals, and the core idea is to go beyond the node-text pairs that come with data. Specifically, we design three augmentation techniques, i.e., node perturbation, text matching, and semantics negation to provide more reference nodes for each text and vice versa. Node perturbation adds/drops edges to produce diversified node embeddings that can be matched with a text. Text matching retrieves texts with similar embeddings to match with a node. Semantics negation uses a negative prompt to construct a negative text with the opposite semantics, which is contrasted with the original node and text. We evaluate Hound on 5 datasets and compare with 13 state-of-the-art baselines. The results show that Hound consistently outperforms all baselines, and its accuracy improvements over the best-performing baseline are usually over 5%. Show Chinese abstract

Abstract: 具有节点文本描述的图（TAG）是一种重要的图结构化数据类型。在TAG上的少量和零样本节点分类在学术界和社会网络等领域有许多应用。然而，由于缺乏监督信号，这两个任务极具挑战性，并且现有方法仅使用对比损失来对齐基于图的节点嵌入和基于语言的文本嵌入。本文提出Hound通过引入更多监督信号来提高准确性，核心思想是超越随数据提供的节点-文本对。具体而言，我们设计了三种增强技术，即节点扰动、文本匹配和语义否定，以分别为每个文本提供更多的参考节点，反之亦然。节点扰动通过添加/删除边来生成可以与文本匹配的不同节点嵌入。文本匹配检索具有相似嵌入的文本以与节点匹配。语义否定使用负提示构造具有相反语义的负面文本，将其与原始节点和文本进行对比。我们在5个数据集上评估Hound并与13种最先进的基线进行了比较。结果显示，Hound始终优于所有基线，其相对于最佳基线的准确性提升通常超过5%。