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[2602.14602] OPBench: A Graph Benchmark to Combat the Opioid Crisis
Summary
OPBench introduces a comprehensive benchmark for evaluating graph learning methods aimed at addressing the opioid crisis, featuring five datasets across three key application domains.
Why It Matters
The opioid crisis is a pressing public health issue, and OPBench provides essential tools for researchers to systematically evaluate and improve graph learning methods. By offering a standardized framework and diverse datasets, it aims to enhance the effectiveness of computational solutions in combating drug-related challenges.
Key Takeaways
- OPBench is the first benchmark specifically designed for the opioid crisis.
- It includes five datasets across overdose detection, trafficking detection, and misuse prediction.
- The benchmark supports heterogeneous graphs and hypergraphs to capture complex relationships.
- A unified evaluation framework ensures reproducibility and fair comparisons among methods.
- Collaboration with experts enhances dataset quality while adhering to ethical guidelines.
Computer Science > Machine Learning arXiv:2602.14602 (cs) [Submitted on 16 Feb 2026] Title:OPBench: A Graph Benchmark to Combat the Opioid Crisis Authors:Tianyi Ma, Yiyang Li, Yiyue Qian, Zheyuan Zhang, Zehong Wang, Chuxu Zhang, Yanfang Ye View a PDF of the paper titled OPBench: A Graph Benchmark to Combat the Opioid Crisis, by Tianyi Ma and 6 other authors View PDF HTML (experimental) Abstract: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...