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NeurIPS 2024PosterAccept (poster)

Practical Shuffle Coding

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

University College London · Meta - FAIR · University of Amsterdam

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摘要

We present a general method for lossless compression of unordered data structures, including multisets and graphs. It is a variant of shuffle coding that is many orders of magnitude faster than the original and enables 'one-shot' compression of single unordered objects. Our method achieves state-of-the-art compression rates on various large-scale network graphs at speeds of megabytes per second, efficiently handling even a multi-gigabyte plain graph with one billion edges. We release an implementation that can be easily adapted to different data types and statistical models.