Sparse Implementation of Versatile Graph-Informed Layers

arXiv:2403.13781v1 Announce Type: new
Abstract: Graph Neural Networks (GNNs) have emerged as effective tools for learning tasks on graph-structured data. Recently, Graph-Informed (GI) layers were introduced to address regression tasks on graph nodes, extending their applicability beyond classic GNNs. However, existing implementations of GI layers lack efficiency due to dense memory allocation. This paper presents a sparse implementation of GI layers, leveraging the sparsity of adjacency matrices to reduce memory usage significantly. Additionally, a versatile general form of GI layers …

abstract arxiv beyond cs.lg cs.na data efficiency gnns graph graph neural networks however implementation math.na memory networks neural networks nodes paper regression structured data tasks tools type