Johannes Lutzeyer
École Polytechnique
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".
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:
We welcome many kinds of papers, such as, but not limited to:
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/ECMLPKDDWT2026. 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.
| 10.30h | Welcoming |
| 10.40h | Keynote
Johannes Lutzeyer Generalisation of Graph Neural Networks |
| 11.40h | Contributed Talk
Marek Cerny Caterpillar GNN: Replacing Message Passing with Graph-Level Aggregation. |
| 12.00h | Contributed Talk
Raffaele Poje, Andrea Passerini, Kim Guldstrand Larsen, Manfred Jaeger Collective Node Classification through Neuro-Symbolic Integration. |
| 12.20h | Spotlight Talks (Group A) |
| 12.30h | Poster Session (Group A) |
| 13.30h | Lunch Break |
| 14.30h | Contributed Talk
Peter Blohm, Florian Chen, Aristides Gionis, Stefan Neumann On the Best Interval Approximation Problem. |
| 14.50h | Contributed Talk
Florian Seiffarth On the Edit Path to GNN Decisions. |
| 15.10h | Spotlight Talks (Group B) |
| 15.20h | Poster Session (Group B) |
| 16.00h | Coffee Break |
| 16.40h | Keynote
Martin Ritzert How to Generate Trees and Other Graphs |
| 17.40h | Closing Remarks and Awards |
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.