Edge-Wise Graph-Instructed Neural Networks
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 12 Sep 2024.
Builds on
11 of 21 references.
References · 11
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Citing papers per year: 2026: 1.
1 paper.