  Graph Neural Networks (GNNs) are a form of deep learning that enable a wide
range of machine learning applications on graph-structured data. The learning
of GNNs, however, is known to pose challenges for memory-constrained devices
such as GPUs. In this paper, we study exact compression as a way to reduce the
memory requirements of learning GNNs on large graphs. In particular, we adopt a
formal approach to compression and propose a methodology that transforms GNN
learning problems into provably equivalent compressed GNN learning problems. In
a preliminary experimental evaluation, we give insights into the compression
ratios that can be obtained on real-world graphs and apply our methodology to
an existing GNN benchmark.
