Since their introduction, graph attention networks achieved outstanding results in graph
representation learning tasks. However, these networks consider only pairwise relations …

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a
given graph topology by dynamically learning it. However, most of LGI methods assume to …

Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly
accommodate higher-order interactions and features. Topological deep learning (TDL) has …

In this work we introduce a convolution operation over the tangent bundle of Riemann
manifolds in terms of exponentials of the Connection Laplacian operator. We define tangent …

Topological neural networks (TNNs) are information processing architectures that model
representations from data lying over topological spaces (eg, simplicial or cell complexes) …

This work extends the theory of reciprocal diagrams in graphic statics to frameworks that are
invariant under finite group actions by utilizing the homology and representation theory of …

The aim of this paper is to introduce a novel dictionary learning algorithm for sparse
representation of signals defined over regular cell complexes. Leveraging tools from Hodge …

Spatial Temporal Graph Convolution Networks (ST-GCNs) have been proposed to embed
spatio-temporal graphs. However, these networks used the Euclidean space as the …

Vector fields are widely used to represent and model flows for many science and
engineering applications. This paper introduces a novel neural network architecture for …

Topological Deep Learning seeks to enhance the predictive performance of neural network
models by harnessing topological structures in input data. Topological neural networks …