Add to ORKGWe propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are supplemented with positional encodings derived from the graph topology and jointly evolved by the Beltrami flow, producing simultaneously continuous feature learning and topology evolution. The resulting model generalises many popular graph neural networks and achieves state-of-the-art results on several benchmarks
Convolutional layers within graph neural networks operate by aggregating information about local nei...
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isot...
The classical development of neural networks has been primarily for mappings between a finite-dimens...
Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing a...
In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric...
Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing a...
Cellular sheaves equip graphs with a "geometrical" structure by assigning vector spaces and linear m...
In the thesis, we propose machine learning algorithms utilising diffusion processes to learn the pai...
Many works have been proposed in the literature to capture the dynamics of diffusion in networks. Wh...
We propose structure-preserving neural-network-based numerical schemes to solve both $L^2$-gradient ...
Graph diffusion is the process of spreading information from one or few nodes to the rest of the gra...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
International audienceDeep Learning algorithms have recently received a growing interest to learn fr...
Convolutional layers within graph neural networks operate by aggregating information about local nei...
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isot...
Convolutional layers within graph neural networks operate by aggregating information about local nei...
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isot...
The classical development of neural networks has been primarily for mappings between a finite-dimens...
Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing a...
In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric...
Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing a...
Cellular sheaves equip graphs with a "geometrical" structure by assigning vector spaces and linear m...
In the thesis, we propose machine learning algorithms utilising diffusion processes to learn the pai...
Many works have been proposed in the literature to capture the dynamics of diffusion in networks. Wh...
We propose structure-preserving neural-network-based numerical schemes to solve both $L^2$-gradient ...
Graph diffusion is the process of spreading information from one or few nodes to the rest of the gra...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
International audienceDeep Learning algorithms have recently received a growing interest to learn fr...
Convolutional layers within graph neural networks operate by aggregating information about local nei...
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isot...
Convolutional layers within graph neural networks operate by aggregating information about local nei...
Existing approaches for diffusion on graphs, e.g., for label propagation, are mainly focused on isot...
The classical development of neural networks has been primarily for mappings between a finite-dimens...