Keynotes and talks (Bâtiment Eiffel, Amphi V)
9h30 – 10h: Welcome Coffee
10h – 11h: Keynote talk by Bastian Grossenbacher-Rieck (Université de Fribourg/Universität Freiburg), Geometry and Topology in the Era of Deep Learning: Revisiting “The Bitter Lesson”
11h – 11h20: Aref Einizade: Tensorial Partial Differential Equations on Graphs
11h20 – 11h40: Ka Man (Ambrose) Yim: Stable and Consistent Spectral Features of Vertices
11h40 – 12h: Hugo Attali: When Should GNNs Look Further? Rethinking Rewiring and Non-Local Dependencies
12h – 13h30: Lunch break
13h30 – 14h30: Keynote talk by Dorina Thanou (EPFL), Beyond Forecasting: Toward Understanding Complex Systems
14h30 – 14h50: Marek Cerny: Unlocking Lower-Order Inductive Biases with Caterpillar GNNs
14h50 – 15h10: Sonia Mazelet: Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs
15h10 – 15h30: Valentin Noël: Geometry of Reason: Spectral Signatures of Valid Mathematical Reasoning
Posters
15h30 – 18h: Poster session & Social time
Ka Man (Ambrose) Yim, "Stable and Consistent Spectral Features of Vertices"
Marek Cerny, "Unlocking Lower-Order Inductive Biases with Caterpillar GNNs"
Salah-Eddine Rizki, "Counterfactual Explanations for Equivariant Graph Neural Networks: CE-EGNN for Molecular Property Regression"
Léo Soudre, "Uncovering the directed graph of a logistic network from sparse trajectories"
Martin Sadric, "Explainable Graph Neural Networks for Power System Applications"
Aurelien Hazan, "Feature-topology dependence and graph-Learning. A contrastive-free SSL architecture"
Sayan Chaki, "Relations are Channels: Kraus Channels For Knowledge Graph Embedding"
Giuseppe Alessio D’Inverno, "Message Passing and Diffusion in Higher-Order Networks"
Mahdi Tavassoli, "Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network"
Dorian Gailhard, "Feature-Aware (Hyper)graph Generation via Next-Scale Prediction"
Antonin Joly, "Taxonomy of reduction matrices for Graph Coarsening"
Adam Ghalem, "Approximating SOCP Solver Trajectories Using Graph Neural Networks"
Vahan Martirosyan, "Generalization Bounds for Spectral GNNs via Fourier Domain Analysis"
Ali Ramlaoui, "TriForces: Augmenting Atomistic GNNs for Transferable Representations"
Antoine Vialle, "Scaling Higher-Order Graph Learning with Maximal Clique Complexes"
Raghuwansh Raj, "Capturing Topological Blindspots for Scalable Multi Cellular Invariant Networks"
Victor Gertner, "AlphaSurf: On-the-Fly Surface Computations for Protein Representation Learning"
Seyed Mohamad Moghadas, "Improving Spatio-Temporal Residual Error Propagation by Mitigating Over-Squashing"