Entropy Coding of Unordered Data Structures
This addresses compression challenges for researchers and practitioners working with unordered data structures in fields like chemistry and network analysis, though it appears incremental as it builds on existing bits-back coding techniques.
The paper tackles the problem of compressing unordered data structures like multisets and graphs by introducing shuffle coding, a method using bits-back coding that achieves state-of-the-art compression rates on graph datasets including molecular data.
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.