Towards the application of Backpropagation-Free Graph Convolutional Networks on Huge Datasets
Nicolò Navarin, Luca Pasa, Alessandro Sperduti · 2024
Backpropagation-Free Graph Convolutional Networks (BF-GCN) are backpropagation-free neural models dealing with graph data based on Gated Linear Networks.Each neuron in a BF-GCN is defined as a set of graph convolution filters (weight vectors) and a gating mechanism that, given a node's context, selects the weight vector to use for processing the node's attributes based on its distance from a set of prototypes.Given the higher expressivity BF-GNN's neurons compared to the standard graph convolutional neural networks' ones, they show bigger memory footprint.In this paper, we explore how reducing the size of node contexts through randomization can reduce the memory occupancy of the method, enabling its application to huge datasets.We empirically show how working with very low dimensional contexts does not impact the resulting predictive performances.* We acknowledge the support of the projects: "Future AI Research (FAIR) -Spoke 2 Integrative AI -Symbolic conditioning of Graph Generative Models (SymboliG)" funded by the European Union under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 -Call for tender No. 341 of March 15, 2022 of Italian Ministry of University and Research -NextGenerationEU, Code PE0000013, Concession Decree No. 1555 of October 11, 2022 CUP C63C22000770006; "iNEST: Interconnected Nord-Est Innovation Ecosystem" funded under the NRRP, Mission 4 Component 2 Investment 1.5 -Call for tender No. 3277 of 30 December 2021 of Italian Ministry of University and Research -NextGenerationEU, Code ECS00000043, Concession Decree No. 1058 of June 23, 2022, CUP C43C22000340006; the PON R&I 2014-2020 project Smart Waste Treatment founded by the FSE REAC-EU; the project "Lifelong Learning on large-scale and structured data"