The problem of multi-task regression over graph nodes has been recently approached through Graph-Instructed Neural Network (GINN), which is a promising architecture belonging to the subset of message-passing graph neural networks. In this work, we discuss the limitations of the Graph-Instructed (GI) layer, and we formalize a novel edge-wise GI (EWGI) layer. We discuss the advantages of the EWGI layer and we provide numerical evidence that EWGINNs perform better than GINNs over some graph-structured input data, like the ones inferred from the Barabasi-Albert graph, and improve the training regularization on graphs with chaotic connectivity, like the ones inferred from the Erdos-Renyi graph.
@article{arxiv.2409.08023,
title = {Edge-Wise Graph-Instructed Neural Networks},
author = {Francesco Della Santa and Antonio Mastropietro and Sandra Pieraccini and Francesco Vaccarino},
journal= {arXiv preprint arXiv:2409.08023},
year = {2025}
}