OPBench: A Graph Benchmark to Combat the Opioid Crisis

2026-02-16

AI Summary

This work addresses the lack of a systematic evaluation benchmark for graph learning methods in the context of the opioid crisis. To bridge this gap, we introduce OPBench, the first multi-domain graph learning benchmark specifically designed for opioid-related research, encompassing three real-world scenarios: medical claims, online drug transactions, and dietary patterns. OPBench integrates heterogeneous graphs and hypergraphs, and provides standardized data splits, reproducible baseline models, and a unified evaluation protocol. Developed through expert collaboration and adhering to privacy-compliant data annotation practices, OPBench enables fair and reliable comparison of graph learning approaches. Extensive experiments reveal significant performance disparities among existing methods on opioid-related tasks, establishing a critical foundation and practical guidance for future research in this domain.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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 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.

Problem

• opioid crisis
• graph benchmark
• drug-related phenomena
• overdose detection
• data scarcity

Innovation

• graph benchmark
• opioid crisis
• heterogeneous graph
• hypergraph
• systematic evaluation