The natural world is full of complex systems characterized by intricate relations between
their components: from social interactions between individuals in a social network to …

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

We systematically investigate graph transformations that enable standard message passing
to simulate state-of-the-art graph neural networks (GNNs) without loss of expressivity. Using …

Graph-based machine learning is experiencing explosive growth, driven by impressive
recent developments and wide applicability. Typical approaches for graph representation …

We propose a simplicial complex convolutional neural network (SCCNN) to learn data
representations on simplicial complexes. It performs convolutions based on the multi-hop …

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 …

The identification of influential simplices is crucial for understanding higher-order network
dynamics. Yet, despite relatively mature research on influential nodes (0-simplices) mining …

Graph Neural Networks (GNNs), despite achieving remarkable performance across different
tasks, are theoretically bounded by the 1-Weisfeiler-Lehman test, resulting in limitations in …

This paper presents the computational challenge on topological deep learning that was
hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning …

The types of spaces where data resides-graphs, meshes, grids, manifolds-are becoming
increasingly varied and heterogeneous. Therefore, translating ideas, models, and …