HGOE：用于图分布外检测的混合外部与内部图异常暴露
HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection
AI总结：
本文提出HGOE框架，通过混合使用外部真实图数据和ID子组内合成的内部异常样本，并设计边界感知OE损失自适应加权，以模型无关的方式显著提升现有图OOD检测模型在8个真实数据集上的性能。
摘要：
随着深度图学习的不断进步，图数据的分布外（OOD）检测已成为一项关键挑战。虽然辅助数据集在增强图像和文本数据OOD检测方面的有效性已被广泛研究，但此类方法尚未在图数据上得到探索。与欧几里得数据不同，图数据表现出更大的多样性，但对扰动的鲁棒性较低，这使得异常样本的整合变得更加复杂。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）来提升图OOD检测性能。我们的框架利用来自不同领域的真实外部图数据，并在ID子组内合成内部异常样本，以解决ID类内鲁棒性差和存在OOD样本的问题。此外，我们设计了一种边界感知的OE损失，能够自适应地为异常样本分配权重，在最大化利用高质量OOD样本的同时，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
