1 code implementation • 28 Dec 2023 • Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, Enza Messina
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 graphs) and the complex-valued Sign-Magnetic Laplacian (proposed to handle digraphs with weights of arbitrary sign).
1 code implementation • 26 May 2022 • Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, Enza Messina
$L^{\sigma}$ is also completely parameter-free, which is not the case of other Laplacian operators such as, e. g., the Magnetic Laplacian.
no code implementations • 9 May 2022 • Vera Colombo, Alessandro Tundo, Michele Ciavotta, Leonardo Mariani
In such environments, monitoring solutions have to cope with the heterogeneity of the devices and platforms present in the Fog, the limited resources available at the edge of the network, and the high dynamism of the whole Cloud-to-Thing continuum.
no code implementations • 11 May 2021 • Federica Filippini, Danilo Ardagna, Marco Lattuada, Edoardo Amaldi, Michele Ciavotta, Maciek Riedl, Katarzyna Materka, Paweł Skrzypek, Fabrizio Magugliani, Marco Cicala
Artificial Intelligence (AI) and Deep Learning (DL) algorithms are currently applied to a wide range of products and solutions.
no code implementations • 1 Mar 2021 • Stefano Fiorini, Michele Ciavotta, Andrea Maurino
In recent years, studying and predicting alternative mobility (e. g., sharing services) patterns in urban environments has become increasingly important as accurate and timely information on current and future vehicle flows can successfully increase the quality and availability of transportation services.