  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.
