Furkejuvvon:
| Váldodahkkit: | , , , |
|---|---|
| Materiálatiipa: | Preprint |
| Almmustuhtton: |
2024
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| Fáttát: | |
| Liŋkkat: | https://arxiv.org/abs/2409.08023 |
| Fáddágilkorat: |
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Sisdoallologahallan:
- 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.