OPBench: A Graph Benchmark to Combat the Opioid Crisis
Tianyi Ma, Yiyang Li, Yiyue Qian, Zheyuan Zhang, Zehong Wang, Chuxu Zhang, and Yanfang Ye

TL;DR
OPBench is a comprehensive graph benchmark designed to evaluate graph learning methods across multiple real-world opioid crisis scenarios, aiding the development of computational solutions to this urgent public health issue.
Contribution
This paper introduces OPBench, the first systematic benchmark with diverse datasets, evaluation protocols, and baselines for modeling opioid-related phenomena using graph learning.
Findings
Existing methods have varying strengths and limitations.
The benchmark facilitates fair comparison of graph learning techniques.
Insights guide future research in opioid crisis modeling.
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…
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Taxonomy
TopicsOpioid Use Disorder Treatment · Machine Learning in Healthcare · HIV, Drug Use, Sexual Risk
