Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribution (ID) data. However, acquiring high-quality labeled nodes in text-attributed graphs (TAGs) is challenging and costly due to their complex textual and structural characteristics. Large language models (LLMs), known for their powerful zero-shot capabilities in textual tasks, show promise but struggle to naturally capture the critical structural information inherent to TAGs, limiting their direct effectiveness. To address these challenges, we propose LLM-GOOD, a general framework that effectively combines the strengths of LLMs and GNNs to enhance data efficiency in graph OOD detection. Specifically, we first leverage LLMs' strong zero-shot capabilities to filter out likely OOD nodes, significantly reducing the human annotation burden. To minimize the usage and cost of the LLM, we employ it only to annotate a small subset of unlabeled nodes. We then train a lightweight GNN filter using these noisy labels, enabling efficient predictions of ID status for all other unlabeled nodes by leveraging both textual and structural information. After obtaining node embeddings from the GNN filter, we can apply informativeness-based methods to select the most valuable nodes for precise human annotation. Finally, we train the target ID classifier using these accurately annotated ID nodes. Extensive experiments on four real-world TAG datasets demonstrate that LLM-GOOD significantly reduces human annotation costs and outperforms state-of-the-art baselines in terms of both ID classification accuracy and OOD detection performance.

翻译：现有的图分布外检测方法通常依赖于使用大量标记的分布内数据来训练图神经网络分类器。然而，由于文本属性图复杂的文本和结构特性，获取高质量的标记节点既困难又成本高昂。大语言模型以其在文本任务中强大的零样本能力而闻名，展现出潜力，但难以自然地捕捉文本属性图固有的关键结构信息，这限制了其直接应用的有效性。为应对这些挑战，我们提出了LLM-GOOD，这是一个通用框架，能有效结合大语言模型和图神经网络的优点，以提升图分布外检测的数据效率。具体而言，我们首先利用大语言模型的强大零样本能力过滤掉可能的分布外节点，从而显著减轻人工标注负担。为最小化大语言模型的使用量和成本，我们仅用它标注一小部分未标记节点。随后，我们使用这些带有噪声的标签训练一个轻量级图神经网络过滤器，使其能够通过利用文本和结构信息，高效预测所有其他未标记节点的分布内状态。在从图神经网络过滤器获得节点嵌入后，我们可以应用基于信息量的方法来选择最有价值的节点进行精确的人工标注。最后，我们使用这些准确标注的分布内节点训练目标分布内分类器。在四个真实世界的文本属性图数据集上进行的大量实验表明，LLM-GOOD显著降低了人工标注成本，并在分布内分类准确率和分布外检测性能方面均优于最先进的基线方法。