  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 is introduced, enabling their application to subsets
of graph nodes. The proposed sparse implementation improves the concrete
computational efficiency and scalability of the GI layers, permitting to build
deeper Graph-Informed Neural Networks (GINNs) and facilitating their
scalability to larger graphs.
