The GRADES-NDA workshop explores the challenges, application areas, and usage scenarios of managing large-scale graph-shaped data. It provides a forum for exchanging ideas on mining, querying, and learning from real-world network data, fostering interdisciplinary collaboration, and sharing datasets and benchmarks. GRADES-NDA brings together researchers from academia, industry, and government to discuss advances in large-scale graph data management and analytics. Its scope covers domain-specific challenges, noise handling in real-world graphs, and innovations in databases, data mining, machine learning, data streaming, network science, and graph algorithms. Case studies across diverse areas are welcome, including Social Networks, Business Analytics, Healthcare, and Cybersecurity.
All papers must be original and not simultaneously submitted to another journal or conference. Submissions must follow the latest 2-column ACM Primary Article Template and have to be anonymous. Categories include Archival (Full papers max 8 pages, Short/Demo/Case studies max 4 pages) and Non-archival (max 4 pages). GRADES-NDA 2026 will recognize outstanding submissions with a Best Paper Award.
Aarhus University & Copenhagen Center for Social Data Science, Denmark
ENSIIE & Télécom SudParis, France
Amazon Web Services & Linköping University, Sweden
University of Waterloo, Canada
Boston University, US
TU Eindhoven, The Netherlands
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