Entropy Coding of Unordered Data Structures

Julius Kunze, Daniel Severo, Giulio Zani, Jan-Willem van de Meent, James Townsend

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Keywords: graph compression, entropy coding, neural compression, bits-back coding, lossless compression, generative models, information theory, probabilistic models, graph neural networks, multiset compression, asymmetric numeral systems, compression, entropy, shuffle coding

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TL;DR: We present shuffle coding, a general method for optimal compression of unordered objects, achieving state-of-the-art compression rates on a range of graph datasets including molecular data.

Abstract: We present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data.

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Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

Submission Number: 4023