Michele Ciavotta — accepted AI-conference papers

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 6 Source ↗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 30 Source ↗PDF ↗code ↗Scholar ↗