Edge-Wise Graph-Instructed Neural Networks
<|reference_start|>Edge-Wise Graph-Instructed Neural Networks: 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 graph-structured input data with chaotic connectivity, like the ones inferred from the Erdos-R\'enyi graph.<|reference_end|>
@article{della santa2024edge-wise,
    title={Edge-Wise Graph-Instructed Neural Networks},
    author={Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini,
  Francesco Vaccarino},
    journal={arXiv preprint arXiv:2409.08023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08023},
    primaryClass={cs.LG cs.AI cs.NA math.NA}
}