5 citations · 9 across the 12 of their papers we have counts for
12 papers
Learn to Jump: Adaptive Random Walks for Long-Range Propagation through Graph Hierarchies
Joël Mathys, Federico Errica
Message-passing architectures struggle to sufficiently model long-range dependencies in node and graph prediction tasks. We propose a novel approach exploiting hierarchical graph s…
Synthetic Data for Blood Vessel Network Extraction
Joël Mathys, Andreas Plesner, Jorel Elmiger +1
Blood vessel networks in the brain play a crucial role in stroke research, where understanding their topology is essential for analyzing blood flow dynamics. However, extracting de…
Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
Niccolò Grillo, Andrea Toccaceli, Joël Mathys +3
Despite incredible progress, many neural architectures fail to properly generalize beyond their training distribution. As such, learning to reason in a correct and generalizable wa…
GraphFSA: A Finite State Automaton Framework for Algorithmic Learning on Graphs
Florian Grötschla, Joël Mathys, Christoffer Raun +1
Many graph algorithms can be viewed as sets of rules that are iteratively applied, with the number of iterations dependent on the size and complexity of the input graph. Existing m…
CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
Florian Grötschla, Joël Mathys, Robert Veres +1
Graph Visualization, also known as Graph Drawing, aims to find geometric embeddings of graphs that optimize certain criteria. Stress is a widely used metric; stress is minimized wh…
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
Stefan Künzli, Florian Grötschla, Joël Mathys +1
Simulating fluid dynamics is crucial for the design and development process, ranging from simple valves to complex turbomachinery. Accurately solving the underlying physical equati…
Flood and Echo Net: Algorithmically Aligned GNNs that Generalize
Joël Mathys, Florian Grötschla, Kalyan Varma Nadimpalli +1
Most Graph Neural Networks follow the standard message-passing framework where, in each step, all nodes simultaneously communicate with each other. We want to challenge this paradi…
SALSA-CLRS: A Sparse and Scalable Benchmark for Algorithmic Reasoning
Julian Minder, Florian Grötschla, Joël Mathys +1
We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing scalability and the utilization of sparse representations. Many algorithms in CLRS require global…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Abstract Visual Reasoning Enabled by Language
Giacomo Camposampiero, Loic Houmard, Benjamin Estermann +2
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexi…
Learning Graph Algorithms With Recurrent Graph Neural Networks
Florian Grötschla, Joël Mathys, Roger Wattenhofer
Classical graph algorithms work well for combinatorial problems that can be thoroughly formalized and abstracted. Once the algorithm is derived, it generalizes to instances of any…
Hierarchical Graph Structures for Congestion and ETA Prediction
Florian Grötschla, Joël Mathys
Traffic4cast is an annual competition to predict spatio temporal traffic based on real world data. We propose an approach using Graph Neural Networks that directly works on the roa…