With the progressive advancements in deep graph learning, out-of-distribution (OOD) detection for graph data has emerged as a critical challenge. While the efficacy of auxiliary datasets in enhancing OOD detection has been extensively studied for image and text data, such approaches have not yet been explored for graph data. Unlike Euclidean data, graph data exhibits greater diversity but lower robustness to perturbations, complicating the integration of outliers. To tackle these challenges, we propose the introduction of \textbf{H}ybrid External and Internal \textbf{G}raph \textbf{O}utlier \textbf{E}xposure (HGOE) to improve graph OOD detection performance. Our framework involves using realistic external graph data from various domains and synthesizing internal outliers within ID subgroups to address the poor robustness and presence of OOD samples within the ID class. Furthermore, we develop a boundary-aware OE loss that adaptively assigns weights to outliers, maximizing the use of high-quality OOD samples while minimizing the impact of low-quality ones. Our proposed HGOE framework is model-agnostic and designed to enhance the effectiveness of existing graph OOD detection models. Experimental results demonstrate that our HGOE framework can significantly improve the performance of existing OOD detection models across all 8 real datasets.

翻译：随着深度图学习的逐步发展，图数据的分布外（OOD）检测已成为一个关键挑战。尽管辅助数据集在提升图像和文本数据的OOD检测性能方面已得到广泛研究，此类方法在图数据上尚未得到探索。与欧几里得数据不同，图数据表现出更高的多样性但对扰动的鲁棒性较低，这使得异常样本的整合变得复杂。为应对这些挑战，我们提出引入**混合外部与内部图异常暴露**（HGOE）以提升图OOD检测性能。我们的框架涉及使用来自不同领域的真实外部图数据，并在分布内（ID）子群内合成内部异常样本，以解决鲁棒性差及ID类别内存在OOD样本的问题。此外，我们开发了一种边界感知的异常暴露损失函数，能够自适应地为异常样本分配权重，从而最大化高质量OOD样本的利用率，同时最小化低质量样本的影响。我们提出的HGOE框架与模型无关，旨在增强现有图OOD检测模型的有效性。实验结果表明，我们的HGOE框架能够在全部8个真实数据集上显著提升现有OOD检测模型的性能。