Analysing the Update step in Graph Neural Networks via Sparsification
Changmin Wu, Johannes F. Lutzeyer, Michalis Vazirgiannis · 2021
In recent years, Message-Passing Neural Networks (MPNNs), the most prominent Graph Neural Network (GNN) framework, have celebrated much success in the analysis of graph-structured data. In MPNNs the computations are split into three steps, Aggregation, Update and Readout. In this paper a series of models to successively sparsify the linear transform in the Update step is proposed. Specifically, the ExpanderGNN model with a tuneable sparsification rate and the Activation-Only GNN, which has no linear transform in the Update step, are proposed. In agreement with a growing trend in the relevant literature the sparsification paradigm is changed by initialising sparse neural network architectures rather than expensively sparsifying already trained architectures. These novel benchmark models enable a better understanding of the influence of the Update step on model performance and outperform existing simplified benchmark models such as the Simple Graph Convolution (SGC). The ExpanderGNNs, and in some cases the Activation-Only models, achieve performance on par with their vanilla counterparts on several down-stream graph prediction tasks, while containing exponentially fewer trainable parameters. In experiments with matching parameter numbers our benchmark models outperform the state-of-the-art GNNs models. These observations enable us to conclude that in practice the Update step often makes no positive contribution to the model performance.