Francesco Della Santa, Antonio Mastropietro, Sandra Pieraccini, Francesco Vaccarino
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引用次数: 0
摘要
在这项工作中,我们讨论了图引导(GI)层的局限性,并正式提出了一种新颖的边缘引导 GI(EWGI)层。我们讨论了 EWGI 层的优势,并提供了数值证据,证明 EWGINN 在处理具有混沌连接性的图结构输入数据(如从 Erdos-R\'enyi 图推断出的数据)时比 GINN 表现更好。
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.