Events

Upcoming events

18th November 2026

Flock: A Knowledge Graph Foundation Model via Learning on Random Walks

Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak, Kyungbin Min, Michael M. Bronstein, Seunghoon Hong, Ismail Ilkan Ceylan

link to the paper

Bridging Input Feature Spaces Towards Graph Foundation Models

Moshe Eliasof, Krishna Sri Ipsit Mantri, Beatrice Bevilacqua, Bruno Ribeiro, Carola-Bibiane Schönlieb

link to the paper

Past Events

Watch Recordings

14th October 2026

The first GLOW poster session

Instead of the usual setup, we have organised a big virtual poster session where we saw lots of interesting posters about graph-related ideas, projects and papers. People could move around the space, drop into conversations, listen to pitches, and discuss — much more informal and exploratory!

This was open to everyone: whether they've been working on something for a while or wanted feedback on early-stage ideas.

We had the following posters:

Panthadeep Bhattacharjee, Sandeep Vidyapu:

BiMass: Towards Batch-incremental Clustering using Mass-based dissimilarity

Dmitry Eremeev Oleg Platonov Gleb Bazhenov Artem Babenko Liudmila Prokhorenkova:

GraphPFN: A Prior-Data Fitted Graph Foundation Model

Julia Gastinger, Thilo Dieing, Christian Meilicke, Heiner Stuckenschmidt:

A Temporal Knowledge Graph for Music Festival Lineup Forecasting

Lea Karbevska:

PACT: Paths, Activity and Contacts for temporal heterogeneous link prediction

Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly:

Latent Diffusion Pretraining for Crystal Property Prediction

Christian Meilicke, Rainer Gemulla, Julie Naegelen, Heiner Stuckenschmidt:

A Simple Baseline for Knowledge Base Completion

Paolo Pellizzoni, Till Hendrik Schulz, Karsten Borgwardt:

Graph Neural Networks Can (Often) Count Substructures

Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev, Liudmila Prokhorenkova:

A Fair Evaluation of Graph Foundation Models for Node Property Prediction

Hamed Shirzad, Danica J. Sutherland:

Global Communication or Graph-Specific Memory?

30th September 2026

On the trade-off between expressivity and privacy in graph representation learning

Patrick Indri, Tamara Drucks, Thomas Gärtner

link to the paper recording

Unifying Graph Neural Networks Through a Common Layer Equation

Sai Karthik Navuluru, Siddhartha Shankar Das, Bo Ni, Hongjie Chen, Yu Wang, Baris Coskunuzer, Nesreen K. Ahmed, Franck Dernoncourt, Mahantesh Halappanavar, Tyler Derr, Ryan A. Rossi, Lakshman Tamil

link to the paper

17th June 2026

Canonical Tree Cover Neural Networks for Expressive and Invariant Graph Learning

Michael Ito, Danai Koutra, Jenna Wiens

link to the paper recording

Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance

Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis

link to the paper recording

11th March 2026

GNN Explanations that do not Explain and How to find Them

Steve Azzolin, Stefano Teso, Bruno Lepri, Andrea Passerini, Sagar Malhotra

link to the paper recording

Understanding Feature/Structure Interplay in Graph Neural Networks

Diana Gomes, Ann Nowe, Peter Vrancx

link to the paper recording

11th February 2026

Memorization in Graph Neural Networks

Adarsh Jamadandi, Jing Xu, Adam Dziedzic, Franziska Boenisch

link to the paper recording

Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs

Sonia Mazelet, Rémi Flamary, Bertrand Thirion

link to the paper recording

26th November 2025

Geometry-Aware Edge Pooling for Graph Neural Networks

Katharina Limbeck, Lydia Mezrag, Guy Wolf, Bastian Rieck

link to the paper recording

What Expressivity Theory Misses: Message Passing Complexity for GNNs

Niklas Kemper, Tom Wollschläger, Stephan Günnemann

link to the paper recording

29th October 2025

GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring

Celia Rubio-Madrigal, Adarsh Jamadandi, and Rebekka Burkholz

link to the paper recording

Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent

Lorenz Kummer, Samir Moustafa, Sebastian Schrittwieser, Wilfried Gansterer, Nils Kriege

link to the paper recording

3rd September 2025

This time, we will have a three-part presentation and discussion. Based on the paper below, we will look into

Oversmoothing
Homophily vs. Heterophily
Oversquashing

Each part will be a short presentation followed by an open discussion.

Oversmoothing, “Oversquashing”, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning

Adrián Arnaiz-Rodríguez, Federico Errica

preprint recording

2nd July 2025

Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms

Baran Hashemi, Kurt Pasque, Chris Teska, Ruriko Yoshida

preprint

No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets

Corinna Coupette, Jeremy Wayland, Emily Simons, Bastian Rieck

preprint

28th May 2025

On Incorporating Scale into Graph Neural Networks

Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael Bronstein, Daniel Cremers

preprint recording

On the Expressive Power of Sparse Geometric MPNNs

Yonatan Sverdlov, Nadav Dym

preprint recording

26th March 2025

LLMs as GNNs (to understand how they generalize)

recording available on Youtube

Problems such as efficient (length) generalization are highly relevant for building agentic systems, yet they have puzzled LLM researchers for several years. However, they can sometimes be obvious through the lens of graph representation learning. This is because LLMs are, by their design, vulnerable to generalization issues from a variety of geometric perspectives.

A typical GNN researcher will already likely have a solid understanding of most of these perspectives! In this talk, we will use this knowledge, and attempt to scale the mountain of LLM generalization ⛰️

Petar Veličković

Research Scientist

Google Deepmind

19th February 2025

Discussion Session on the Future of Graph Learning Research

We want to use the February session to discuss what kind of research we want to do in graph learning in the next years and where we can make the most impact. Similar questions have already been discussed at length on big stages at various conferences. However, we think it is time to have this discussion with all graph learning enthusiasts, in particular (incoming) PhD students and postdocs who are actively shaping the field and whose research comprises the majority of what will be the field of graph learning in the next years. Whether you are excited, skeptical, curious, or frustrated about graph learning, we want to hear what you have to say!