GraphAware: Interpretable machine learning on graphs

GraphAware: Interpretable machine learning on graphs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GraphAware: Interpretable machine learning on graphs Daniel Walke, Daniel Steinbach, Alexander Schönhuth, Gunter Saake, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7471432/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted 11 You are reading this latest preprint version Abstract Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in many applications from disease prediction to weather forecasting. However, each GNN layer introduces new trainable parameters that increase its complexity and limit its interpretability. To address these limitations, we propose GraphAware, a new framework that enables an efficient and interpretable analysis of graph-structured data. GraphAware uses easily customizable aggregation functions to aggregate neighborhood features without training which are then passed to standard machine learning classifiers compatible with established interpretability frameworks such as SHAP. We show that GraphAware achieves competitive classification performance compared to state-of-the-art GNNs like Graph Attention Networks on transductive and inductive graph benchmarks. In addition, we demonstrate that the returned results are highly interpretable, improving decision making, transparency, error analysis, and trust in trained models. GraphAware can use popular Python packages such as scikit-learn and XGBoost and uses a scikit-learn-like API to increase usability. The complete framework is open-source on https://github.com/danielwalke/GraphAware. Graphs Graph learning Machine learning Classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 01 Dec, 2025 Reviews received at journal 27 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 10 Nov, 2025 Reviewers agreed at journal 08 Nov, 2025 Reviewers agreed at journal 06 Nov, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 27 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7471432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517875593,"identity":"c347b0ce-de28-42a1-8b8b-302f7fe0acc2","order_by":0,"name":"Daniel 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