186 citations · 451 across the 24 of their papers we have counts for
5 papers · 1 filter
GATSY: Graph Attention Network for Music Artist Similarity
Andrea Giuseppe Di Francesco, Giuliano Giampietro, Indro Spinelli +1
The artist similarity quest has become a crucial subject in social and scientific contexts, driven by the desire to enhance music discovery according to user preferences. Modern re…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
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 competi…
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Claudio Battiloro, Indro Spinelli, Lev Telyatnikov +3
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 have…
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Indro Spinelli, Michele Guerra, Filippo Maria Bianchi +1
Subgraph-enhanced graph neural networks (SGNN) can increase the expressive power of the standard message-passing framework. This model family represents each graph as a collection…
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
Indro Spinelli, Riccardo Bianchini, Simone Scardapane
The rise of graph representation learning as the primary solution for many different network science tasks led to a surge of interest in the fairness of this family of methods. Lin…