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:<!doctype html>
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<title>GRADES-NDA 2026</title>
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<a href="/"><img src="img/sigmod-banner-2.jpg" alt="GRADES-NDA" style="width:80%; height:40%"></a>
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<div id="title-container">9th Joint Workshop on Graph Data Management
Experiences & Systems (GRADES) and Network Data Analytics (NDA)
</div>
<div id="subtitle-container"> Co-located with <a href="https://2026.sigmod.org/"> SIGMOD/PODS 2026 </a> (June 5, 2026, Bengaluru, India)
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<ul>
<li><a href="#accepted" class="bordered">Accepted Papers</a></li>
<!-- <li><a href="#">Registration</a></li>-->
<li><a href="#dates">Important Dates</a></li>
<li><a href="#program" class="bordered">Program</a></li>
<!-- <li><a href="https://dl.acm.org/doi/proceedings/10.1145/3461837" class="bordered">Proceedings</a></li> -->
<!-- <li><a href="#submission">Paper Submission</a></li> -->
<li><a href="#pc">Program Committee</a></li>
<li><a href="#travel">Travel Awards</a></li>
<li><a href="#previous">Past Workshops</a></li>
<li><a href="#sponsors">Sponsors</a></li>
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<div id="content">
<section class="row">
<div class="col-full">
<h2>Call for Papers</h2>
<p> 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.
<!-- The workshop highlights technical contributions in graph data management systems, emphasizing practical implementations over isolated techniques. -->
</p>
<p> 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.
</p>
<p>Topics of interest include but are not limited to the following.</p>
<ul>
<li>Graph modeling and processing – advances in representing, visualizing, storing, indexing, querying, and managing graph data.</li>
<li>Graph query languages, visualization, and querying interfaces – design, usability, practical implementations, and use cases.</li>
<li>Knowledge Graphs – construction, augmentation, reasoning, and neuro-symbolic approaches.</li>
<li>GenAI techniques – integration of Knowledge Graphs and LLMs for information retrieval, question answering, knowledge inference, and natural language understanding.</li>
<li>Graph processing platforms – including Titan, Giraph, GraphChi, SPARK/GraphX, GraphLab/PowerGraph, and others.</li>
<li>Human-centric graph processing – interactive approaches for graph data exploration, querying, and analytics.</li>
<li>Reliable graph data processing – validation and verification techniques for ensuring the trustworthiness of algorithms, query languages, applications, and systems.</li>
<li>Graph metrics – methods for measuring graph characteristics, e.g., diameter, eigenvalues, triangle counting.</li>
<li>Spatial and temporal graph analytics – updates, dynamic graphs, streaming analytics, evolution tracking, point-of-interest recommendation, community structure detection, etc.</li>
<li>Graph mining and machine learning – including heterogeneous networks and knowledge graphs.</li>
<li>Graph summarization and sampling – efficient methods for large-scale data.</li>
<li>Noisy and uncertain graphs – analytics on incomplete, inconsistent, or unreliable data.</li>
<li>Network dynamics – game theory, social contagion, and information propagation.</li>
<li>Domain-specific graph analytics – applications in social networks, biology, business, finance, healthcare, transportation, etc.</li>
<li>Vision and systems papers – potential or real applications of graph management, especially in the era of large language models.</li>
</ul>
<br>
<p> Accepted archival papers will be published by ACM, indexed by DBLP, and will be available in the ACM DL.</p>
</div>
</section>
<!--
\item \emph{Graph modeling and processing}: Advances in techniques for representing, visualizing, storing, indexing, querying, and managing graph data.
\item \emph{RDF and Graph Databases:}
\begin{itemize}
\item Data and/or index structures.
\item Proposals of benchmarks for RDF and graph database workloads and their performance evaluation on diverse data management systems.
\item Query processing and optimization algorithms for RDF and graph database systems.
\item System descriptions.
\end{itemize}
\item \emph{Dynamic and temporal graphs}:
\begin{itemize}
\item Efficient and reliable change updates in graph data.
\item Managing evolving networks and their applications.
\item Analysing evolution and detection of community structures in real-world graphs.
\item Spatio-temporal graph analytics.
\end{itemize}
\item \emph{Graph metrics}: Methods for measuring graph characteristics, e.g., diameter, eigenvalues, triangle counting.
\item \emph{Scalable graph processing}: Innovations in technologies for large-scale graph analytics.
\begin{itemize}
\item \emph{Core graph platforms and parallel computing frameworks}: Exploration of graph processing platforms and parallel computing technologies, e.g., Titan, Giraph, GraphChi, SPARK/GraphX, GraphLab/PowerGraph, with a focus on scalability, performance optimization, and practical applications.
\item \emph{Mining and machine learning on heterogeneous networks}: Techniques for extracting insights and patterns from heterogeneous networks, with applications in clustering, link prediction, anomaly detection, representation learning, etc.
\item Experiences or techniques for graph-specific operations such as traversals or inference/reasoning in the context of large data sets and on the systems that implement those operations.
\item Graph summarization and sampling
\end{itemize}
\item \emph{Human-centric graph processing: Interactive} techniques and human-in-the-loop approaches to enhance graph data exploration, querying, and analytics.
\item \emph{GenAI techniques}: Integration of Knowledge Graphs and Large Language Models for information retrieval, question answering, knowledge inference, and natural language understanding.
\item \emph{Neuro-symbolic approaches}: Hybrid techniques that combine Graph Neural Networks (GNNs) with rule-based systems for reasoning and analytics.
\item \emph{Reliability and security}: Verification and validation tools and techniques for trustworthy graph data processing, as well as security aspects, such as differential privacy and blockchain/graph-based ledgers.
\item \emph{Education}: Best practices for teaching emerging graph technologies, experience reports from educators, and innovative approaches to training students and practitioners.
\item \emph{Applications}: Descriptions of graph data management use cases and query workloads, and experiences with applying data management technologies in various areas, including but not limited to:
\begin{itemize}
\item Social Networks; Citation Networks; Co-Purchase Networks
\item Game Theory, Social contagion and Information propagation on networks
\item Biological Network Data; Ecological data
\item Retail, Marketing, and Media
\item Financial Services and Business Data Analysis
\item Customer Care; Healthcare; Transportation data
\item Cybersecurity
\end{itemize}
\item \emph{Vision papers} describing potential applications and benefits of graph management.
-->
<section class="row">
<div class="col-full">
<h2><a id="accepted"></a>Accepted Papers (Archival)</h2>
<ul>
<li>Hiroki Fukuzawa: Keio University; Tsuyoshi Yamashita: Keio University; Kunitake Kaneko: Keio University.<br> <b>Fast and Accurate Random Walk Index Maintenance for Graph Merging</b>.</li>
<li>Diego Rivera Correa: Northeastern University; Laurent Bindschaedler: MPI-SWS.<br> <b>Informed Search for Regular Path Query Reachability</b>.</li>
<li>Kazuhisa Natori: Keio University; Tsuyoshi Yamashita: Keio University; Kunitake Kaneko: Keio University.<br> <b>PerPreX: Fast Personalized Graph Summarization by Pre-executed Multi-Level Graph Division</b>.</li>
<li>Hrishikesh Haritas: Indian Institute of Science; Pranjal Naman: Indian Institute of Science; Mohit Agrawal: National Payments Corporation of India; Saurav Singla: National Payments Corporation of India; Yogesh Simmhan: Indian Institute of Science.<br> <b>Computing Temporal Graph Centrality Measures over Billion-scale Financial Networks</b>.</li>
<li>Diego Arroyuelo: Pontificia Universidad Católica de Chile; José Cazorla: Universidad Técnica Federico Santa María; Gonzalo Navarro: University of Chile.<br> <b>Boosting Graph Joins and Matrix Multiplications in Little Space</b>.</li>
<li>Harishankar K. Nair: Vellore Institute of Technology, Chennai; Rithika Naveencharan: Vellore Institute of Technology, Chennai; Kshreya Meenakshisundar: Indian Institute of Science Education and Research (IISER), Pune; Reena Monica P: Vellore Institute of Technology, Chennai.<br> <b>Hop-Wise Adaptive Spectral Filters for Graph Representation Learning</b>.</li>
<li>Makbule Gulcin Ozsoy: Neo4j.<br> <b>Incremental Multilingual Text2Cypher with Adapter Combination</b>.</li>
<li>Rithika Naveencharan: Vellore Institute of Technology, Chennai; Harishankar K. Nair: Vellore Institute of Technology, Chennai; Kshreya Meenakshisundar: Indian Institute of Science Education and Research (IISER), Pune; Reena Monica P: Vellore Institute of Technology, Chennai.<br> <b>Decoupled Graph Attention Networks</b>.</li>
</ul> <br>
<h2>Accepted Papers (Non-Archival)</h2>
<ul>
<li>Vishwajeet Bharadwaj: PSG College of Technology; Kiruthika V G: PSG College of Technology; Latha R: PSG College of Technology.<br> <b>A PageRank Framework for Capturing Local Influence in Clustered Networks</b>.</li>
</ul>
</div>
</section>
<!--
<section class="row">
<div class="col-full">
<h2><a id="accepted"></a>Accepted Papers (Archival)</h2>
<ul>
<li>Naima Abrar Shami and Vasiliki Kalavri.<br> <b>Bridging GNN Inference and Dataflow Stream Processing: Challenges and Opportunities</b>.</li>
<li>Bishwajit Bhattacharjee, Nafis Ahmed, Sujaya Maiyya, and Renee Miller. <br> <b>Towards Oblivious Property Graph Databases</b>.</li>
<li>Simon Grätzer, Lars Heling, and Pavel Klinov.<br> <b>BARQ: A Vectorized SPARQL Query Execution Engine</b>.</li>
<li>Janik Hammerer and Wim Martens.<br> <b>A Compendium of Regular Expression Shapes in SPARQL Queries</b>.</li>
<li>Leonid Libkin, Cristina Sirangelo, and Deniz Yilmaz.<br> <b>Extending Pattern Matching Queries in Property Graphs with Interpreted Predicates</b>.</li>
<li>Hrishikesh Terdalkar, Angela Bonifati, and Andrea Mauri.<br> <b>Graph Repairs with LLMs: An Empirical Study</b>.</li>
<li>Chongyang Xu and Laurent Bindschaedler.<br> <b>Everything You Wanted to Know About Graph Neural Network Partitioning (But Were Afraid to Ask)</b>.</li>
<li>Hadar Rotschield and Liat Peterfreund.<br> [Short Research Paper] <b>Towards Cross-Model Efficiency in SQL/PGQ</b>.</li>
</ul> <br>
<h2>Accepted Papers (Non-Archival)</h2>
<ul>
<li>Shaoshuai Du, Joze Rozanec, Ana Lucia Varbanescu, and Andy D. Pimentel.<br> <b>Understanding Streaming Graph Processing Systems: a Comparative Study of Models, Performance, and Trade-offs</b>.</li>
<li>Cheng Huang, Johannes Langguth, Davide Mottin, and Ira Assent.<br> <b>GCore: A Fast GPU-parallelized Approach to D-Core Decomposition</b>.</li>
<li>Dmytro Lopushanskyy and Borun Shi.<br> <b>Graph Neural Networks on Graph Databases</b>.</li>
<li>Larissa Shimomura, George Fletcher, Hiroaki Shiokawa, Toshiyuki Amagasa, and Md Abu Marjan.<br> <b>Towards Documentation Guidelines for Property Graphs</b>.</li>
<li>Srinitish Srinivasan and Omkumar Cu.<br> <b>Lorentzian Graph Isomorphic Network</b>.</li>
</ul>
</div>
</section>
-->
<section class="row">
<div class="col-full">
<h2>Keynote Speakers</h2>
<p> We are honored to have the following keynote speakers to talk about their exciting research in the broad fields of network science and of graph data management.</p>
<ul>
<li><a href="https://www.unsw.edu.au/staff/wenjie-zhang">Wenjie Zhang</a> The University of New South Wales, Australia</li>
<li><a href="https://www.cs.ubc.ca/people/laks-vs-lakshmanan">Laks V.S. Lakshmanan</a> The University of British Columbia, Canada</li>
</ul>
</div>
</section>
<section class="row">
<div class="col-full">
<h2><a id="program"></a>Program</h2>
<p style="color:gray"><i>All times are in IST (Indian Standard Time, UTC/GMT + 5:30 hours).</i></p>
<p class="session">
<div class="separator"><b>8:30-8:45 Opening Remarks</b></div>
</p>
<p class="session">
<span class="time">8:45-10:30</span>
<span class="stitle">Session 1</span>
</p>
<p class="session">
<span class="time">8:45-9:45</span>
<span class="stitle">AI Meets Graphs: From AI-Accelerated Graph Analytics to Graph-Enhanced Generation - <i>Wenjie Zhang</i></span><img src="img/wenjie.jpg" alt="Wenjie Zhang" height="200" width="200" id="bio-pic" />
<span class="abstract"><b>Abstract:</b>
Graphs have long been the foundation for modeling complex interconnected data, from knowledge networks and scientific datasets to social and e-commerce platforms. As these systems scale in size and complexity, research has increasingly focused on scalable graph data processing and analytics. At the same time, artificial intelligence is reshaping how we analyze and reason over data. In this talk, I will outline the evolving landscape in which learning-based techniques, including graph neural networks (GNNs) and large language models (LLMs), contribute to improving the efficiency and flexibility of graph analytics. In a complementary direction, I will also present how graph-structured data retrieval augments LLMs, enabling more reliable multi-hop reasoning for complex generation tasks. This talk highlights the emerging synergy between AI and graph data, forming a mutually reinforcing paradigm that opens new research opportunities and future directions.
</span>
<span class="bio"><b>Speaker bio:</b>
Dr. Wenjie Zhang is a Professor in the School of Computer Science and Engineering at the University of New South Wales, Sydney. Her main research interests lie in large-scale data management and the applications. Dr. Zhang serves as an Associate Editor for IEEE TKDE, VLDB Journals and ACM TKDD. She has served on numerous organization and program committees for international conferences, including PC co-chair of ICDE 2025, APWeb-WAIM 2024 and WISE 2021, as well as Area Chair for VLDB, ICDE and ICDM. Currently, she is Chair of the Steering Committee for the Australasian Database Conference. Her research has been recognized with the ACM SIGMOD Research Highlight Award, CORE Chris Wallace Research Award, and 19 Best Paper Awards or nominations from conferences including SIGMOD and ICDE. Dr. Zhang is an elected Member of the CORE Academy and a Fellow of the Australian Computer Society and the Royal Society of NSW Australia.
</span>
</p>
<p class="session">
<span class="time">9:50-10:30</span>
<span class="stitle">Graph Analytics at Scale</span>
</p>
<ul>
<li class="paper">
<span class="title">[9:50-10:15] Fast and Accurate Random Walk Index Maintenance for Graph Merging.</span>
<span class="authors">Hiroki Fukuzawa, Tsuyoshi Yamashita, and Kunitake Kaneko.</span>
</li>
<li class="paper">
<span class="title">[10:15-10:30] Computing Temporal Graph Centrality Measures over Billion-scale Financial Networks.</span>
<span class="authors">Hrishikesh Haritas, Pranjal Naman, Mohit Agrawal, Saurav Singla, and Yogesh Simmhan.</span>
</li>
</ul>
<p class="session">
<div class="separator">Coffee Break + free-roam posters (30 mins)</div>
</p>
<p class="session">
<span class="time">11:00-11:50</span>
<span class="stitle">Session 2</span>
</p>
<p class="session">
<span class="time">11:00-11:50</span>
<span class="stitle">Efficient Graph Computation</span>
</p>
<ul>
<li class="paper">
<span class="title">[11:00-11:25] PerPreX: Fast Personalized Graph Summarization by Pre-executed Multi-Level Graph Division.</span>
<span class="authors">Kazuhisa Natori, Tsuyoshi Yamashita, and Kunitake Kaneko.</span>
</li>
<li class="paper">
<span class="title">[11:25-11:50] Boosting Graph Joins and Matrix Multiplications in Little Space.</span>
<span class="authors">Diego Arroyuelo, José Cazorla, and Gonzalo Navarro.</span>
</li>
</ul>
<p class="session">
<span class="time">11:50-12:30</span>
<span class="stitle">Poster Presentations</span>
</p>
<p class="session">
<div class="separator">Lunch + free-roam posters (1 hour)</div>
</p>
<p class="session">
<span class="time">13:30-15:00</span>
<span class="stitle">Session 3</span>
</p>
<p class="session">
<span class="time">13:30-14:30</span>
<span class="stitle">Toward an Open Map of the World: Can Knowledge Graphs Help? - <i>Laks V.S. Lakshmanan</i></span><img src="img/laks.jpg" alt="Laks V.S. Lakshmanan" height="200" width="200" id="bio-pic-2" />
<span class="abstract"><b>Abstract:</b>
The use of digital maps is pervasive in our day-to-day lives. They play a pivotal role in applications such as navigation, fleet management, and ride-sharing, accentuating the need for their accuracy and currency. Unfortunately maps such as Google Maps, boasting high accuracy and coverage, tend to be proprietary. OpenStreetMap provides an open alternative thus promoting “creative, productive, and unexpected” uses of maps. However, its coverage can be variable in that some entities may be missing even in areas where it otherwise has strong coverage. Map conflation is the process of augmenting a geospatial database (GDB) with another GDB to “conflate” missing spatial features, the idea being that with successive conflation steps applied to a “master” GDB with other high-accuracy GDBs that have high local coverage, its coverage and currency can be continually improved. Existing map conflation methods are heuristic algorithmic approaches that are based on pre-defined rules, unable to learn matching entities in a data-driven manner. In this talk, I will talk about KRAFT (Knowledge gRAph-based Framework for automated map conflaTion), a machine-learning approach for map conflation leveraging knowledge graphs that was developed in our group. I will overview the challenges, our design, and the techniques developed, including the use of knowledge graph representation, support for both linear and non-linear map objects, and consistency-aware map merging. I will also highlight the strong empirical performance of KRAFT over baselines, before concluding with open problems and future directions. This is joint work with Gorisha Agrawal and Farnoosh Hashemi.
</span>
<span class="bio"><b>Speaker bio:</b>
Laks V.S. Lakshmanan is a professor of Computer Science at UBC, Vancouver, Canada. His research interests span a wide spectrum of topics in data management, integration, cleaning, and warehousing; data mining; semi-structured and unstructured data; big graphs, social networks and social media; ML, NLP; and efficient deep learning. He is an ACM Distinguished Scientist and has won several awards including best paper awards and distinguished reviewer awards. He has served on most top conferences and journals in his areas of research, on program committees, as senior PC member, meta-reviewer, general chair, and as associate editor. He currently serves as Associate Editor of the Information Systems Journal, the VLDB Journal, Proceedings of the VLDB Endowment (PVLDB) 2026, and Proceedings of ACM on Management of Data (PACMMOD) 2026, and as Meta-reviewer or Senior PC Member of AAAI 2026 and ACM RecSys Conference 2025.
</span>
</p>
<p class="session">
<span class="time">14:30-15:00</span>
<span class="stitle">Graph Query Processing</span>
</p>
<ul>
<li class="paper">
<span class="title">[14:30-14:55] Informed Search for Regular Path Query Reachability.</span>
<span class="authors">Diego Rivera Correa and Laurent Bindschaedler.</span>
</li>
</ul>
<p class="session">
<div class="separator">Coffee Break + free-roam posters (30 mins)</div>
</p>
<p class="session">
<span class="time">15:30-16:45</span>
<span class="stitle">Session 4</span>
</p>
<p class="session">
<span class="time">15:30-16:45</span>
<span class="stitle">Neural Approaches for Graph Querying and Representation</span>
</p>
<ul>
<li class="paper">
<span class="title">[15:30-15:55] Incremental Multilingual Text2Cypher with Adapter Combination.</span>
<span class="authors">Makbule Gulcin Ozsoy.</span>
</li>
<li class="paper">
<span class="title">[15:55-16:20] Hop-Wise Adaptive Spectral Filters for Graph Representation Learning.</span>
<span class="authors">Harishankar K. Nair, Rithika Naveencharan, Kshreya Meenakshisundar, and Reena Monica P.</span>
</li>
<li class="paper">
<span class="title">[16:20-16:45] Decoupled Graph Attention Networks.</span>
<span class="authors">Rithika Naveencharan, Harishankar K. Nair, Kshreya Meenakshisundar, and Reena Monica P.</span>
</li>
</ul>
<p class="session">
<div class="separator"><b>16:45-17:00 Best Paper Award 🏆</b></div>
</p>
</div>
</section>
<section class="row">
<div class="col">
<h2><a id="dates"></a>Important Dates</h2>
<ul>
<li><s>Abstract Submission: March 08, 2026</s></li>
<li><s>Full Paper Submission: March 17, 2026</li> (Note that the deadline has been extended and it is possible to submit everything at once)</s></li>
<li><s>Notifications: April 13, 2026</s></li>
<li><s>Camera Ready Submission: April 26, 2026</s></li>
<li>Workshop Date: June 5, 2026</li>
</ul>
<p></p>
<i>All deadlines are 23:59 AoE.</i>
<p></p>
</div>
<div class="col">
<h2>Workshop Organizers</h2>
<ul>
<li><a href="https://cs.au.dk/~clan/people/aarora">Akhil Arora</a>, Aarhus University & Copenhagen Center for Social Data Science, Denmark</li>
<li><a href="https://web4.ensiie.fr/~stefania.dumbrava">Stefania Dumbrava</a>, ENSIIE & Télécom SudParis, France</li>
</ul><br>
<h2>Steering Committee</h2>
<ul>
<li><a href="http://olafhartig.de/">Olaf Hartig</a>, Amazon Web Services (AWS) & Linköping University, Sweden</li>
<li><a href="https://cs.uwaterloo.ca/~ssalihog/">Semih Salihoglu</a>, University of Waterloo & Kùzu, Canada</li>
<li><a href="https://cs-people.bu.edu/vkalavri/">Vasiliki Kalavri</a>, Boston University, US</li>
<li><a href="https://www.tue.nl/en/research/researchers/george-fletcher/">George Fletcher</a>, TU Eindhoven, The Netherlands</li>
</ul>
</div>
</section>
<section class="row">
<div class="col-full">
<h2><a id="submission"></a>Paper Submission</h2>
<p>Authors are invited to submit original, unpublished research papers in the following categories: </p>
<ul>
<li><b>Archival</b> : Accepted papers under this category will be published by the ACM, indexed by DBLP, and will be available in the ACM DL.</li>
<ul>
<li>Regular (long) papers should be a maximum of 8 pages, excluding references and appendix.</li>
<li>Short papers, demonstration papers, and vision papers should be a maximum of 4 pages, excluding references and appendix.</li>
<li>Case studies should be a maximum of 4 pages, excluding references and appendix.</li>
</ul> <br>
<li><b>Non-archival</b> : Accepted papers under this category will not be published in the proceedings, but will be listed on the website.</li>
<ul>
<li>Papers that are suitable for this category are work-in-progress papers presenting early results.<br>
These papers should be a maximum of 4 pages in length, excluding references and appendix.</li>
</ul>
</ul> <br>
🆕 <font color="red">This year we are also excited to introduce a <b>Best Paper Award</b> to recognize outstanding contributions to the field!</font> 🏆 <br> <br>
<p> 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" </p>
<p>Submissions must follow the latest 2-column <a href="https://www.acm.org/publications/proceedings-template">ACM Primary Article Template</a> (<a href="https://www.overleaf.com/latex/templates/association-for-computing-machinery-acm-sig-proceedings-template/bmvfhcdnxfty">Overleaf template</a>). </p>
<p>Reviewing will be double-anonymous, for which the submissions must be anonymized by following the same anonymity requirements as for <a href="https://2026.sigmod.org/calls_papers_sigmod_research.shtml">regular track papers at the SIGMOD/PODS 2026 conference</a>. </p>
<p>You can use the following LaTeX command to compile your paper without author names:</p>
<p><code>\documentclass[sigconf, anonymous, review]{acmart}</code>.</p>
<p>Submissions that do not follow these requirements will be desk-rejected.</p>
<p>Submissions will be handled through CMT.
<p>The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
<p><b>Submission process</b> : to submit click <a href="https://cmt3.research.microsoft.com/GRADESNDA2026">here</a>.</p>
</p>
</section>
<section class="row">
<div class="col-full">
<h2><a id="pc"></a>Program Committee</h2>
<ul>
<li> <b>Renzo Angles</b>, Universidad de Talca, Chile </li>
<li> <b>Amitabha Bagchi</b>, Indian Institute of Technology Delhi, India</li>
<li> <b>Kaustubh Beedkar</b>, Indian Institute of Technology Delhi, India</li>
<li> <b>Maciej Besta</b>, ETH Zurich, Switzerland</li>
<li> <b>James Clarkson</b>, Neo4j, USA </li>
<li> <b>Juan A. Colmenares</b>, Microsoft Fabric Graph, USA </li>
<li> <b>Sourav Dutta</b>, Huawei Research Center, UK </li>
<li> <b>Lisa Ehrlinger</b>, Hasso Plattner Institute, University of Potsdam, Germany </li>
<li> <b>Tushar Goyal</b>, Microsoft, USA</li>
<li> <b>Russ Harmer</b>, Ecole Normale Supérieure de Lyon, France </li>
<li> <b>Adriana Iamnitchi</b>, Maastricht University, The Netherlands</li>
<li> <b>Milos Jovanovik</b>, TU Wien, Austria</li>
<li> <b>Vasiliki Kalavri</b>, Boston University, USA</li>
<li> <b>Panos Kalnis</b>, King Abdullah University of Science and Technology, Saudi Arabia</li>
<li> <b>Meike Klettke</b>, Universität Regensburg, Germany</li>
<li> <b>Silviu Maniu</b>, Université Grenoble Alpes, LIG, CNRS, France </li>
<li> <b>Ioana Manolescu</b>, INRIA, Institut Polytechnique de Paris, France </li>
<li> <b>Victor Marsault</b>, Université Gustave Eiffel, CNRS, LIGM, France </li>
<li> <b>Andrea Mauri</b>, Université Lyon 1, LIRIS, CNRS, France </li>
<li> <b>Amine Mhedhbi</b>, Polytechnique Montréal, Canada </li>
<li> <b>Nikos Ntarmos</b>, Huawei Technologies R&D, UK </li>
<li> <b>Makoto Onizuka</b>, University of Osaka, Japan </li>
<li> <b>Marcus Paradies</b>, Ludwig-Maximilians-Universität München, Germany </li>
<li> <b>Matei Ripeanu</b>, University of British Columbia, Canada </li>
<li> <b>Alexandra Rogova</b>, Uniwersytet Warszawski, Poland </li>
<li> <b>Semih Salihoglu</b>, University of Waterloo, Kùzu, Canada </li>
<li> <b>Hiroaki Shiokawa</b>, University of Tsukuba, Japan </li>
<li> <b>Petra Selmer</b>, Bloomberg, UK </li>
<li> <b>Genoveva Vargas-Solar</b>, Université Lyon 1, LIRIS, CNRS, France </li>
<li> <b>Hannes Voigt</b>, Neo4j, Germany </li>
<li> <b>Yinghui Wu</b>, Case Western Reserve University, USA </li>
<li> <b>Nikolay Yakovets</b>, Eindhoven University of Technology, The Netherlands </li>
<li> <b>Chao Zhang</b>, University of Waterloo, Canada </li>
<!--
<li> <b>Shubhangi Agarwal</b>, Université Lyon 1, LIRIS, CNRS, France </li>
<li> <b>Renzo Angles</b>, Universidad de Talca, Chile </li>
<li> <b>Amitabha Bagchi</b>, Indian Institute of Technology, India </li>
<li> <b>Srikanta Bedathur</b>, Indian Institute of Technology, India </li>
<li> <b>Kaustubh Beedkar</b>, Indian Institute of Technology, India </li>
<li> <b>Yang Cao</b>, University of Edinburgh, UK </li>
<li> <b>James Clarkson</b>, Neo4j, USA </li>
<li> <b>Sourav Dutta</b>, Huawei Research Center, Ireland </li>
<li> <b>Lisa Ehrlinger</b>, Hasso-Plattner-Institut, Germany </li>
<li> <b>Lorena Etcheverry</b>, Universidad de la República, Uruguay </li>
<li> <b>Sainyam Galhotra</b>, Cornell University, USA </li>
<li> <b>Amélie Gheerbrant</b>, Université de Paris, IRIF, CNRS, France </li>
<li> <b>Paul Groth</b>, University of Amsterdam, The Netherlands </li>
<li> <b>Jan Hidders</b>, Birkbeck College, University of London, UK </li>
<li> <b>Panagiotis Karras</b>, University of Copenhagen, Denmark </li>
<li> <b>Haridimos Kondylakis</b>, ICS-FORTH, Greece </li>
<li> <b>Longbin Lai</b>, Alibaba Group, China </li>
<li> <b>Sahil Manchanda</b>, Pocket FM Data Science Team, India </li>
<li> <b>Silviu Maniu</b>, Université Grenoble Alpes, LIG, CNRS, France </li>
<li> <b>Ioana Manolescu</b>, INRIA, Institut Polytechnique de Paris, France </li>
<li> <b>Victor Marsault</b>, Université Gustave Eiffel, CNRS, LIGM, France </li>
<li> <b>Andrea Mauri</b>, Université Lyon 1, LIRIS, CNRS, France </li>
<li> <b>Amine Mhedhbi</b>, École Polytechnique de Montréal, Canada </li>
<li> <b>Davide Mottin</b>, Aarhus University, Denmark </li>
<li> <b>Serafeim Papadias</b>, TU Berlin, Germany </li>
<li> <b>Marcus Paradies</b>, Ludwig-Maximilians-Universität München, Germany </li>
<li> <b>Liat Peterfreund</b>, Hebrew University, RelationalAI, Israel </li>
<li> <b>Evaggelia Pitoura</b>, University of Ioannina, Greece </li>
<li> <b>Yuya Sasaki</b>, Osaka University, Japan </li>
<li> <b>Semih Salihoglu</b>, University of Waterloo, Kùzu, Canada </li>
<li> <b>Petra Selmer</b>, Bloomberg, UK </li>
<li> <b>Hrishikesh Terdalkar</b>, Université Lyon 1, LIRIS, CNRS, France </li>
<li> <b>Dominik Tomaszuk</b>, University of Bialystok, Poland </li>
<li> <b>Riccardo Tommasini</b>, INSA Lyon, LIRIS, CNRS, France </li>
<li> <b>Georgia Troullinou</b>, Université Grenoble Alpes, France </li>
<li> <b>Ana Lucia Varbanescu</b>, University of Amsterdam, The Netherlands </li>
<li> <b>Genoveva Vargas-Solar</b>, CNRS, Université Lyon 1, LIRIS, France </li>
<li> <b>Nikolay Yakovets</b>, Eindhoven University of Technology, The Netherlands </li>
-->
</ul>
</div>
</section>
<!--
<section class="row">
<div class="col-full">
<h2><a id="pc"></a>Program Committee</h2>
<ul>
<li> Renzo Angles, Universidad de Talca </li>
<li> Marcelo Arenas, PUC Chile </li>
<li> Amitabha Bagchi, Indian Institute of Technology, Delhi </li>
<li> Kaustubh Beedkar, IIT Delhi </li>
<li> Yang Cao, The University of Edinburgh </li>
<li> Nathalie Charbel, Neo4j </li>
<li> Juan Colmenares, Microsoft </li>
<li> Sourav Dutta, Huawei Research </li>
<li> George H. L. Fletcher, Eindhoven University of Technology </li>
<li> Russ Harmer, CNRS & ENS Lyon </li>
<li> Jan Hidders, Birkbeck College, University of London </li>
<li> Davide Mottin, Aarhus University </li>
<li> Nikos Ntarmos, Huawei Technologies R&D (UK) Ltd </li>
<li> Evaggelia Pitoura, Univ. of Ioannina </li>
<li> Petra Selmer, Bloomberg </li>
<li> Marco Serafini, University of Massachusetts Amherst </li>
<li> Hiroaki Shiokawa, University of Tsukuba </li>
<li> Vasileios Trigonakis, Oracle Labs </li>
<li> Hannes Voigt, Neo4j </li>
<li> Yinghui Wu, Case Western Reserve University </li>
<li> Yinglong Xia, Facebook </li>
<li> Yuichi Yoshida, National Institute of Informatics </li>
<li> Shangdi Yu, MIT </li>
</ul>
</div>
</section>
-->
<section class="row">
<div class="col-full">
<h2><a id="travel"></a>Student Travel Awards</h2>
<p>Thanks to the generous support of our Sponsors, we are offering awards for selected student authors of accepted GRADES-NDA papers.
Each awardee will receive a stipend to <b>partially cover</b> the expense to attend the conference in-person.
Awardees are expected to register and attend the GRADES-NDA Workshop
and encouraged to attend the SIGMOD conference. Students will have to make their own arrangements for travel and accommodation.
These awards are only for students who can attend in person.
If you cannot attend in person, we advise you to check with <a href="https://2025.sigmod.org/travel_awards.shtml">the SIGMOD travel awards committee</a>.</p>
<p><b>Eligibility:</b> Applicants need to be full-time undergraduate or graduate students.
We will primarily prioritize students whose advisors cannot provide financial support.
We will also prioritize students who are not from North America or Europe, as well as female and minority students. </p>
<p><b>Application Procedure:</b> To apply for a grant, the student must email the necessary materials to GRADES-NDA chairs (at our email address: grades-nda@googlegroups.com) by <b>April 26</b>.
We will notify applicants by <b>April 30</b>.
Please submit the following information in a single PDF file with your application:</p>
<ul>
<li>Your full name, school, and email address.</li>
<li>Your advisor's full name and email address.</li>
<li>Your CV.</li>
<li>An abstract, summarizing your thesis research and its connection to graph data management or graph analytics (at most one page in single column format).</li>
<li>A short letter of support from your advisor.</li>
<li>If you think your presence could help diversity in the GRADES-NDA or SIGMOD community (in terms of the gender, geography/origin, ethnicity or in other ways), please add an additional paragraph with an explanation (does not count towards the one-page limit for your research).</li>
</ul>
</div>
</section>
<section class="row">
<h2 id="previous">Past Workshops</h2>
<p>GRADES-NDA is in its ninth edition, and had successful joint meetings co-located with ACM SIGMOD/PODS from 2018 to 2025. Specifically, it is the merger of the GRADES and NDA workshops, which were each independently organized and successfully held at previous ACM SIGMOD/PODS conferences: GRADES (since 2013) and NDA (since 2017). The organizers of GRADES and NDA mutually agreed upon a joint meeting from 2018 onwards.</p><br>
<div class="col" style="width:30%">
<h2>GRADES-NDA</h2>
<ul>
<li><a href="http://gradesnda.github.io/2025/">GRADES-NDA 2025</a></li>
<li><a href="http://gradesnda.github.io/2024/">GRADES-NDA 2024</a></li>
<li><a href="http://gradesnda.github.io/2023/">GRADES-NDA 2023</a></li>
<li><a href="http://gradesnda.github.io/2022/">GRADES-NDA 2022</a></li>
<li><a href="http://gradesnda.github.io/2021/">GRADES-NDA 2021</a></li>
<li><a href="http://gradesnda.github.io/2020/">GRADES-NDA 2020</a></li>
<li><a href="https://sites.google.com/site/gradesnda2019/">GRADES-NDA 2019</a></li>
<li><a href="https://sites.google.com/site/gradesnda2018/">GRADES-NDA 2018</a></li>
</ul>
</div>
<div class="col" style="width:30%">
<h2>GRADES</h2>
<ul>
<li><a href="https://event.cwi.nl/grades/2017/index.shtml">GRADES 2017</a></li>
<li><a href="https://event.cwi.nl/grades/2016/index.shtml">GRADES 2016</a></li>
<li><a href="https://event.cwi.nl/grades/2015/index.shtml">GRADES 2015</a></li>
<li><a href="https://event.cwi.nl/grades/2014/index.shtml">GRADES 2014</a></li>
<li><a href="https://event.cwi.nl/grades/2013/index.shtml">GRADES 2013</a></li>
</ul>
</div>
<div class="col" style="width:30%">
<h2>NDA</h2>
<ul>
<li><a href="https://sites.google.com/site/networkdataanalytics2017/">NDA 2017</a></li>
<li><a href="https://sites.google.com/site/networkdataanalytics2016/">NDA 2016</a></li>
</ul>
</div>
</section>
<section class="row">
<div class="col-full">
<h2 id="sponsors">Sponsored by (To be confirmed)</h2>
<!--
<div class="photo-grid">
<a href="https://www.amazon.com/" class="col-3"><img src="img/amazon-logo.png" alt="Amazon" width="200"></a>
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<a href="https://www.sap.com/" class="col-3"><img src="img/sap-logo.png" alt="SAP" width="150"></a>
</div>
-->
</div>
</section>
<section class="row">
<div class="col-full-b">
<h2>Get in touch</h2>
<p>
For questions, please <a href="mailto:grades-nda@googlegroups.com">email us</a> 😊. Follow us on <a href="https://bsky.app/profile/did:plc:4wn6us7ull5fscsqxedot2yy">BlueSky</a>, <a href="https://www.linkedin.com/company/grades-nda">LinkedIn</a>, and <a href="https://twitter.com/gradesnda">X</a> for the latest updates.
</p>
</div>
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