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arXiv · 2409.08023

Edge-Wise Graph-Instructed Neural Networks

Francesco Della Santa·Antonio Mastropietro·Sandra Pieraccini·Francesco Vaccarino
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Abstract

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.

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Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini, Francesco Vaccarino. 2024-09-12. Edge-Wise Graph-Instructed Neural Networks. https://doi.org/10.1016/j.jocs.2024.102518

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