Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
<|reference_start|>Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark: 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 Unified Benchmark for unsupervised Graph-level OOD and anomaly Detection (our method), 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 16 representative GLAD/GLOD methods. We conduct multi-dimensional analyses to explore the effectiveness, generalizability, robustness, and efficiency of existing methods, shedding light on their strengths and limitations. Furthermore, we provide an open-source codebase (https://github.com/UB-GOLD/UB-GOLD) of our method to foster reproducible research and outline potential directions for future investigations based on our insights.<|reference_end|>
@article{wang2024unifying,
    title={Unifying Unsupervised Graph-Level Anomaly Detection and
  Out-of-Distribution Detection: A Benchmark},
    author={Yili Wang, Yixin Liu, Xu Shen, Chenyu Li, Kaize Ding, Rui Miao, Ying
  Wang, Shirui Pan, Xin Wang},
    journal={arXiv preprint arXiv:2406.15523},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15523},
    primaryClass={cs.LG stat.ML}
}