To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD) have received significant attention in recent years. Though those two lines of research indeed share the same objective, they have been studied independently in the community due to distinct evaluation setups, creating a gap that hinders the application and evaluation of methods from one to the other. To bridge the gap, in this work, we present a \underline{\textbf{U}}nified \underline{\textbf{B}}enchmark for unsupervised \underline{\textbf{G}}raph-level \underline{\textbf{O}}OD and anoma\underline{\textbf{L}}y \underline{\textbf{D}}etection (\ourmethod), a comprehensive evaluation framework that unifies GLAD and GLOD under the concept of generalized graph-level OOD detection. Our benchmark encompasses 35 datasets spanning four practical anomaly and OOD detection scenarios, facilitating the comparison of 18 representative GLAD/GLOD methods. We conduct multi-dimensional analyses to explore the effectiveness, OOD sensitivity spectrum, robustness, and efficiency of existing methods, shedding light on their strengths and limitations. Furthermore, we provide an open-source codebase (this https URL) of \ourmethod to foster reproducible research and outline potential directions for future investigations based on our insights.
View on arXiv@article{wang2025_2406.15523,
title={ Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark },
author={ Yili Wang and Yixin Liu and Xu Shen and Chenyu Li and Kaize Ding and Rui Miao and Ying Wang and Shirui Pan and Xin Wang },
journal={arXiv preprint arXiv:2406.15523},
year={ 2025 }
}