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arXiv · 2304.14793

Learning Graph Neural Networks using Exact Compression

Jeroen Bollen·Jasper Steegmans·Jan Van den Bussche·Stijn Vansummeren
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Abstract

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.

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Jeroen Bollen, Jasper Steegmans, Jan Van den Bussche, Stijn Vansummeren. 2023-04-28. Learning Graph Neural Networks using Exact Compression. https://doi.org/10.1145/3594778.3594878

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