OPBench：应对阿片危机的图数据基准

OPBench: A Graph Benchmark to Combat the Opioid Crisis

February 16, 2026

作者: Tianyi Ma, Yiyang Li, Yiyue Qian, Zheyuan Zhang, Zehong Wang, Chuxu Zhang, Yanfang Ye

cs.AI

摘要

阿片类药物危机持续肆虐全球社区，给医疗系统带来巨大压力，破坏家庭稳定，亟需高效的计算解决方案。为应对这一致命危机，图学习方法已成为模拟复杂药物相关现象的重要范式。然而当前存在显著空白：缺乏能够在真实阿片危机场景中系统评估这些方法的综合性基准。为此，我们推出首个综合性阿片危机基准OPBench，涵盖三大关键应用领域的五个数据集：基于医疗理赔的阿片过量检测、基于数字平台的非法药物交易识别，以及基于饮食模式的药物滥用预测。具体而言，OPBench整合了异质图和超图等多样化图结构，以保留药物数据间丰富复杂的关系信息。针对数据稀缺问题，我们联合领域专家与权威机构，在遵循隐私和伦理准则的前提下进行数据采集与标注。此外，我们建立了包含标准化协议、预设数据划分和可复现基线模型的统一评估框架，以促进图学习方法的公平系统比较。通过大量实验，我们深入分析了现有图学习方法的优势与局限，为未来应对阿片危机的研究提供了可操作的见解。项目源码与数据集详见https://github.com/Tianyi-Billy-Ma/OPBench。

English

The opioid epidemic continues to ravage communities worldwide, straining healthcare systems, disrupting families, and demanding urgent computational solutions. To combat this lethal opioid crisis, graph learning methods have emerged as a promising paradigm for modeling complex drug-related phenomena. However, a significant gap remains: there is no comprehensive benchmark for systematically evaluating these methods across real-world opioid crisis scenarios. To bridge this gap, we introduce OPBench, the first comprehensive opioid benchmark comprising five datasets across three critical application domains: opioid overdose detection from healthcare claims, illicit drug trafficking detection from digital platforms, and drug misuse prediction from dietary patterns. Specifically, OPBench incorporates diverse graph structures, including heterogeneous graphs and hypergraphs, to preserve the rich and complex relational information among drug-related data. To address data scarcity, we collaborate with domain experts and authoritative institutions to curate and annotate datasets while adhering to privacy and ethical guidelines. Furthermore, we establish a unified evaluation framework with standardized protocols, predefined data splits, and reproducible baselines to facilitate fair and systematic comparison among graph learning methods. Through extensive experiments, we analyze the strengths and limitations of existing graph learning methods, thereby providing actionable insights for future research in combating the opioid crisis. Our source code and datasets are available at https://github.com/Tianyi-Billy-Ma/OPBench.