Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

[2608.14578] Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.14578 (cs) [Submitted on 12 Jun 2026] Title: Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study Authors: Yixuan He , Jinni Su , Yun Kang View a PDF of the paper titled Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study, by Yixuan He and 2 other authors View PDF Abstract: Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitudinal trajectories, and relational context remains unclear. Using data from approximately 11,860 participants in the Adolescent Brain Cognitive Development (ABCD) Study, we compare cross-sectional, longitudinal, and graph-based approaches for predicting alcohol sipping, alcohol use, marijuana use, and alcohol/marijuana use. We evaluate tree-based models, recurrent neural networks, and Temporal Graph Convolutional Networks (T-GCNs) constructed from family, school, and feature-similarity graphs. Longitudinal models consistently outperform baseline models, with temporal XGBoost achieving the strongest standalone performance. Although T-GCNs generally do not surpass temporal XGBoost, graph-derived risk scores provide complementary information. Combining temporal XGBoost and T-GCN predictions through score-level stacking yields the best performance across all outcomes, achieving AUC-ROC values above 0.79. Feature analyses identify peer deviance, age, externalizing symptoms, parental monitoring, cultural norms, and neighborhood context as important predictors of substance use onset. These findings demonstrate the value of longitudinal modeling for substance-use prediction and suggest that graph-based representations can provide effective auxiliary risk signals. Comments: 8 pages main text, 10 pages total, 4 tables Subjects: Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY); Machine Learning (cs.LG); Applications (stat.AP) Cite as: arXiv:2608.14578 [cs.AI] (or arXiv:2608.14578v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14578 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yixuan He [ view email ] [v1] Fri, 12 Jun 2026 05:00:30 UTC (276 KB) Full-text links: Access Paper: View a PDF of the paper titled Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study, by Yixuan He and 2 other authors View PDF view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CY cs.LG stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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