ReorderBench: A Benchmark for Matrix Reordering
<|reference_start|>ReorderBench: A Benchmark for Matrix Reordering: Matrix reordering permutes the rows and columns of a matrix to reveal meaningful visual patterns, such as blocks that represent clusters. A comprehensive collection of matrices, along with a scoring method for measuring the quality of visual patterns in these matrices, contributes to building a benchmark. This benchmark is essential for selecting or designing suitable reordering algorithms for specific tasks. In this paper, we build a matrix reordering benchmark, ReorderBench, with the goal of evaluating and improving matrix reordering techniques. This is achieved by generating a large set of representative and diverse matrices and scoring these matrices with a convolution- and entropy-based method. Our benchmark contains 2,835,000 binary matrices and 5,670,000 continuous matrices, each featuring one of four visual patterns: block, off-diagonal block, star, or band. We demonstrate the usefulness of ReorderBench through three main applications in matrix reordering: 1) evaluating different reordering algorithms, 2) creating a unified scoring model to measure the visual patterns in any matrix, and 3) developing a deep learning model for matrix reordering.<|reference_end|>
@article{zhu2024reorderbench:,
    title={ReorderBench: A Benchmark for Matrix Reordering},
    author={Jiangning Zhu, Zheng Wang, Zhiyang Shen, Lai Wei, Fengyuan Tian,
  Mengchen Liu, Shixia Liu},
    journal={arXiv preprint arXiv:2408.12169},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12169},
    primaryClass={cs.HC}
}