11 citations · 11 across the 5 of their papers we have counts for
3 papers · 1 filter
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
Chenyu Liu, Haige Li, Luca Rossi
Graph Neural Networks (GNNs) are neural networks that aim to process graph data, capturing the relationships and interactions between nodes using the message-passing mechanism. GNN…
Generating Graphs via Spectral Diffusion
Giorgia Minello, Alessandro Bicciato, Luca Rossi +2
In this paper, we present GGSD, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we pro…
GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
Alessandro Bicciato, Luca Cosmo, Giorgia Minello +2
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper, we propose a new graph neural network architecture that sub…