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Stefano Coniglio

5 accepted papers

2026

Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs

Emanuele Mule, Stefano Fiorini, Antonio Purificato, Federico Siciliano, Stefano Coniglio, Fabrizio Silvestri
ICLR 2026poster

Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed hypergraphs, which can model oriented group interactions, remains largely under-explored despite its relevance for many a…

Cited by 0Source ↗code ↗Scholar ↗
2026

Sheaves Reloaded: A Direction Awakening

Stefano Fiorini, Hakan Aktas, Iulia Duta, Pietro Morerio, Alessio Del Bue, Pietro Lio +1
ICLR 2026poster

Sheaf Neural Networks (SNNs) are a powerful algebraic-topology generalization of Graph Neural Networks (GNNs), and have been shown to significantly improve our ability to model complex relational data. While the GNN literature proved that incorporating directionality can substantially boost performa…

Cited by 0Source ↗code ↗Scholar ↗
2024

Graph Learning in 4D: A Quaternion-Valued Laplacian to Enhance Spectral GCNs

Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, Enza Messina
AAAI 2024technical

We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

SigMaNet: One Laplacian to Rule Them All

Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, Enza Messina
AAAI 2023technical

This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign nor magnitude. The cornerstone of SigMaNet is the Sign-Magnetic Laplacian (LSM), a new Laplacian matrix that we introduce ex nov…

Cited by 30Source ↗PDF ↗code ↗Scholar ↗