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.

翻译：图神经网络（GNNs）是一种深度学习形式，能够支持对图结构数据进行广泛的机器学习应用。然而，已知在GPU等内存受限设备上学习GNN会带来挑战。本文研究了利用精确压缩来降低在大规模图上学习GNN的内存需求。具体而言，我们采用形式化方法进行压缩，并提出一种方法论，将GNN学习问题转化为可证明等价的压缩GNN学习问题。在初步实验评估中，我们揭示了在实际图上可获得的压缩率，并将该方法应用于现有的GNN基准测试。