Entropy Coding of Unordered Data Structures
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 16 Aug 2024.
Builds on
11 of 28 references.
References · 11
- 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 718
- 2019Practical Lossless Compression with Latent Variables using Bits Back Codingcited by 85
- 2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
- 2019Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variablescited by 51
- 2021Order Matters: Probabilistic Modeling of Node Sequence for Graph Generationcited by 32
- 2018Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representationscited by 12
- 2021Partition and Code: learning how to compress graphscited by 10
- 2021Lossless Compression with Latent Variable Modelscited by 4
- 2021Compressing Multisets with Large Alphabets using Bits-Back Codingcited by 4
- 2021Parallel Computation of Combinatorial Symmetriescited by 2
- 2023Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphscited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 2.
3 papers.