HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection

TL;DR

This paper introduces HGOE, a novel framework combining external and internal graph outlier exposure to enhance out-of-distribution detection in graph data, addressing robustness and diversity challenges.

Contribution

HGOE is a new, model-agnostic framework that synthesizes internal outliers and utilizes external data with a boundary-aware loss to improve graph OOD detection.

Findings

Significantly improves OOD detection across 8 datasets.

Effectively utilizes external and internal outlier data.

Enhances robustness of graph OOD detection methods.

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.