Call for Papers

The GRADES-NDA workshop explores the challenges, application areas, and usage scenarios of managing large-scale graphs. 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.

Topics of interest include but are not limited to the following.

• Graph modeling and processing – advances in representing, visualizing, storing, indexing, querying, and managing graph data.
• Graph query languages, visualization, and querying interfaces – design, usability, practical implementations, and use cases.
• Knowledge Graphs – construction, augmentation, reasoning, and neuro-symbolic approaches.
• GenAI techniques – integration of Knowledge Graphs and LLMs for information retrieval, question answering, knowledge inference, and natural language understanding.
• Graph processing platforms – including Titan, Giraph, GraphChi, SPARK/GraphX, GraphLab/PowerGraph, and others.
• Human-centric graph processing – interactive approaches for graph data exploration, querying, and analytics.
• Reliable graph data processing – validation and verification techniques for ensuring the trustworthiness of algorithms, query languages, applications, and systems.
• Graph metrics – methods for measuring graph characteristics, e.g., diameter, eigenvalues, triangle counting.
• Spatial and temporal graph analytics – updates, dynamic graphs, streaming analytics, evolution tracking, point-of-interest recommendation, community structure detection, etc.
• Graph mining and machine learning – including heterogeneous networks and knowledge graphs.
• Graph summarization and sampling – efficient methods for large-scale data.
• Noisy and uncertain graphs – analytics on incomplete, inconsistent, or unreliable data.
• Network dynamics – game theory, social contagion, and information propagation.
• Domain-specific graph analytics – applications in social networks, biology, business, finance, healthcare, transportation, etc.
• Vision and systems papers – potential or real applications of graph management, especially in the era of large language models.

Accepted papers will be published by ACM, indexed by DBLP, and will be available in the ACM DL.

Paper Submission

Authors are invited to submit original, unpublished research papers in the following categories:

• Archival : Accepted papers under this category will be published by the ACM, indexed by DBLP, and will be available in the ACM DL.
• Regular (long) papers should be a maximum of 8 pages, excluding references and appendix.
• Short papers, demonstration papers, and vision papers should be a maximum of 4 pages, excluding references and appendix.
• Case studies should be a maximum of 4 pages, excluding references and appendix.


• 🆕 Non-archival : Accepted papers under this category will not be published in the proceedings, but will be listed on the website.
• Papers that are suitable for this category are work-in-progress papers presenting early results.
  These papers should be a maximum of 4 pages in length, excluding references and appendix.

Please indicate the submission type in the title of the paper, e.g., "[Regular Research Paper] XXX", "[Short Research Paper] XXX", "[Demo] XXX", "[Case-Study] XXX", "[Vision] XXX", "[Work-in-progress] XXX"

Submissions must follow the latest 2-column ACM Primary Article Template (Overleaf template).

Reviewing will be double-anonymous, for which the submissions must be anonymized by following the same anonymity requirements as for regular track papers at the SIGMOD/PODS 2025 conference.

You can use the following LaTeX command to compile your paper without author names:

\documentclass[sigconf, anonymous, review]{acmart}.

Submissions that do not follow these requirements will be desk-rejected.

Submissions will be handled through Easychair. To submit click here.

Program Committee

• Shubhangi Agarwal, Université Lyon 1, LIRIS, CNRS, France
• Renzo Angles, Universidad de Talca, Chile
• Amitabha Bagchi, Indian Institute of Technology, India
• Srikanta Bedathur, Indian Institute of Technology, India
• Kaustubh Beedkar, Indian Institute of Technology, India
• Yang Cao, University of Edinburgh, UK
• James Clarkson, Neo4j, USA
• Sourav Dutta, Huawei Research Center, Ireland
• Lisa Ehrlinger, Hasso-Plattner-Institut, Germany
• Lorena Etcheverry, Universidad de la República, Uruguay
• Sainyam Galhotra, Cornell University, USA
• Amélie Gheerbrant, Université de Paris, IRIF, CNRS, France
• Paul Groth, University of Amsterdam, The Netherlands
• Jan Hidders, Birkbeck College, University of London, UK
• Panagiotis Karras, University of Copenhagen, Denmark
• Haridimos Kondylakis, ICS-FORTH, Greece
• Longbin Lai, Alibaba Group, China
• Sahil Manchanda, Pocket FM Data Science Team, India
• Silviu Maniu, Université Grenoble Alpes, LIG, CNRS, France
• Ioana Manolescu, INRIA, Institut Polytechnique de Paris, France
• Victor Marsault, Université Gustave Eiffel, CNRS, LIGM, France
• Andrea Mauri, Université Lyon 1, LIRIS, CNRS, France
• Amine Mhedhbi, École Polytechnique de Montréal, Canada
• Davide Mottin, Aarhus University, Denmark
• Serafeim Papadias, TU Berlin, Germany
• Marcus Paradies, Ludwig-Maximilians-Universität München, Germany
• Liat Peterfreund, Hebrew University, RelationalAI, Israel
• Evaggelia Pitoura, University of Ioannina, Greece
• Yuya Sasaki, Osaka University, Japan
• Semih Salihoglu, University of Waterloo, Kùzu, Canada
• Petra Selmer, Bloomberg, UK
• Hrishikesh Terdalkar, Université Lyon 1, LIRIS, CNRS, France
• Dominik Tomaszuk, University of Bialystok, Poland
• Riccardo Tommasini, INSA Lyon, LIRIS, CNRS, France
• Georgia Troullinou, Université Grenoble Alpes, France
• Ana Lucia Varbanescu, University of Amsterdam, The Netherlands
• Genoveva Vargas-Solar, CNRS, Université Lyon 1, LIRIS, France
• Nikolay Yakovets, Eindhoven University of Technology, The Netherlands

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