Abstract
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.
Abstract (translated)
随着深度图学习的发展，对于图数据的离群（OUD）检测已成为一个关键挑战。虽然辅助数据在增强OUD检测效果方面已经进行了广泛研究，但对于图数据，这种方法还没有被探索。与欧氏数据不同，图数据表现出更大的多样性，但对于干扰的抵抗力较低，这使得在集成异常值时更加复杂。为解决这些挑战，我们提出了Hybrid External and Internal Graph Outlier Exposure (HGOE)来提高图OUD检测性能。我们的框架包括使用各种领域的现实外部图数据，并在ID子组内合成内部异常值来解决ID类中的OOD样本的不足和脆弱性。此外，我们还开发了一个边界感知的外部异常损失，根据异常的边界分配权重，最大程度地利用高质量的OOD样本，同时最小化低质量样本的影响。我们提出的HGOE框架对所有8个真实数据集的现有OUD检测模型具有模型无关性，并旨在增强现有模型的效果。实验结果表明，在我们的HGOE框架下，所有8个真实数据集的现有OUD检测模型的性能都有显著提高。
URL
https://arxiv.org/abs/2407.21742
