Edge-Wise Graph-Instructed Neural Networks

• URL: http://arxiv.org/abs/2409.08023v2
• Date: Wed, 08 Jan 2025 12:40:56 GMT
• Title: Edge-Wise Graph-Instructed Neural Networks
• Authors: Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini, Francesco Vaccarino,
• Abstract summary: We discuss the limitations of the Graph-Instructed (GI) layer, and we formalize a novel edge-wise GI (EWGI) layer. We provide numerical evidence that EWGINNs perform better than GINNs over some graph-structured input data.
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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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