1. Dates
2. Keynote Speakers
3. Call for Papers
4. Schedule
5. Organizers and PC
6. Previous Workshops

23rd International Workshop on
Mining and Learning with Graphs

Important Dates

• Paper submission deadline: 5. June 2026
• Paper acceptance notification: tbd
• Camera ready submission deadline: 10. July 2026
• Workshop date: Monday, 7. September or Friday, 11. September 2026
• All deadlines expire 23:59 AoE

Keynotes

Johannes Lutzeyer
École Polytechnique

Johannes Lutzeyer is an Assistant Professor in the Data Science and Mining Team at the Laboratoire d'Informatique of École Polytechnique (France). He previously completed a postdoc under the supervision of Prof. Michalis Vazirgiannis at École Polytechnique, and did his PhD on the spectral properties of the adjacency and Laplacian matrices under the supervision of Prof. Andrew Walden at Imperial College London. His research focuses on Graph Neural Networks and Spectral Properties of Graph Shift Operator Matrices.

Martin Ritzert
Leipzig University

Martin Ritzert is a Junior Group Leader at Leipzig University (Germany) where his research is on graph machine learning and particularly on graph generation for neuronal morphologies and biological trees. During his PhD at RWTH Aachen University and his postdoc at Aarhus University he worked on the theoretical underpinnings of machine learning, before moving towards more practical machine learning and data science topics at University of Göttingen and Leipzig University. The title of the talk is "Growing beech and oak trees: Generative machine learning for 3D graphs".

Call for Papers

This workshop is a forum for exchanging ideas and methods for mining and learning with graphs, developing new common understandings of the problems at hand, sharing data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia and industry to create a forum to discuss recent advances in graph analysis. In doing so, our aim is to better understand the overarching principles and limitations of current methods and to inspire research on new algorithms and techniques for mining and learning with graphs.

To reflect the broad scope of work on mining and learning with graphs, we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation to applications and empirical studies. We are interested in the full spectrum of graph data, including but not limited to attributed graphs, labeled graphs, knowledge graphs, evolving graphs, transactional graph databases, etc.

We therefore invite submissions on theoretical aspects, algorithms and methods, and applications of the following (non-exhaustive) list of areas:

• Computational or statistical learning theory related to graphs
• Theoretical analysis of graph algorithms or models
• Semi-supervised learning, online learning, active learning, transductive inference, and transfer learning in the context of graphs
• Unsupervised learning and graph clustering
• Interesting pattern mining on graphs and community detection
• Graph kernels and metric learning on graphs
• Graph and vertex embeddings and representation learning on graphs
• Solving combinatorial problems on graphs with ML / data-driven combinatorial optimization
• Explainable, fair, robust, and/or privacy-preserving ML on graphs
• Statistical models of graphs and graph sampling
• Analysis of social media, chemical or biological networks, infrastructure networks, knowledge graphs
• Benchmarking and reproducibility aspects of graph-based learning
• Libraries and tools for all of the above areas

We welcome many kinds of papers, such as, but not limited to:

• Novel research papers
• Demo papers
• Dataset papers
• Work-in-progress papers
• Visionary papers (white papers)
• Appraisal papers of existing methods and tools (e.g., lessons learned)
• Relevant work that has been previously published
• Work that will be presented at the main conference (can be submitted with the regular 16-page limit of ECMLPKDD)

Submission Guidelines: Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions. All papers will be peer-reviewed (single-blind). Submissions must be in PDF, long papers no more than 12 pages long, short papers no more than 8 pages long, formatted according to the standard Springer LNCS style required for ECMLPKDD submissions. References, acknowledgments, and appendix do not count towards the page limit. The accepted papers will be published on the workshop website and will not be considered archival for resubmission purposes. Authors whose papers are accepted to the workshop will have the opportunity to participate in a pitch and poster session, and the best four will also be chosen for oral presentation.

Papers should be submitted via CMT: https://cmt3.research.microsoft.com/ECMLPKDDWorkshopTrack2026. Please select the MLG: Mining and Learning with Graphs track.

Post-Workshop Springer Proceedings: High quality, original, non-dual-submitted papers will be invited to be published in post-workshop proceedings, assuming that ECMLPKDD offers them as in previous years.

Dual Submission Policy: We accept submissions that are currently under review at other venues. However, in this case, our page limits apply. Please also check the dual submission policy of the other venue.

Schedule

Organizers

• 
  Franka Bause
  CISPA
• 
  Thomas Gärtner
  TU Wien
• 
  Caterina Graziani
  University of Siena
• 
  Nils Kriege
  University of Vienna
• 
  Christoph Sandrock
  TU Wien
• 
  Yllka Velaj
  University of Vienna
• 
  Pascal Welke
  Lancaster University Leipzig

Program Committee

• tbc

Previous Workshops

• 2025, Porto, Portugal (co-located with ECMLPKDD)
• 2024, Vilnius, Lithuania (co-located with ECMLPKDD)
• 2023, Torino, Italy (co-located with ECMLPKDD)
• 2023, Long Beach, USA (co-located with KDD)
• 2022, Grenoble, France (co-located with ECMLPKDD)
• 2022, Washington, USA (co-located with KDD)
• 2020, virtual (co-located with KDD)
• 2019, Anchorage, USA (co-located with KDD)
• 2018, London, United Kingdom (co-located with KDD)
• 2017, Halifax, Nova Scotia, Canada (co-located with KDD)
• 2016, San Francisco, USA (co-located with KDD)
• 2013, Chicago, USA (co-located with KDD)
• 2012, Edinburgh, Scotland (co-located with ICML)
• 2011, San Diego, USA (co-located with KDD)
• 2010, Washington, USA (co-located with KDD)
• 2009, Leuven, Belgium (co-located with SRL and ILP)
• 2008, Helsinki, Finland (co-located with ICML)
• 2007, Firenze, Italy
• 2006, Berlin, Germany (co-located with ECML and PKDD)
• 2005, Porto, Portugal, October 7, 2005 (co-located with ECML and PKDD)
• 2004, Pisa, Italy, September 24, 2004 (co-located with ECML and PKDD)
• 2003, Cavtat-Dubrovnik, Croatia (co-located with ECML and PKDD)

This page tries to be minimalistic in layout, bandwith, and used tools. It is hosted on github pages, using neat.css stylesheets, and bibtexparser to generate the lists of papers.