@article{Zhou2025, 
author = {Zi-Han Zhou and Jia-Cheng Pan and Xu-Meng Wang and Dong-Ming Han and Fang-Zhou Guo and Min-Feng Zhu and Wei Chen},
title = {A Summarization-Based Pattern-Aware Matrix Reordering Approach},
year = {2025},
journal = {Journal of Computer Science and Technology},
volume = {40},
number = {5},
pages = {1331-1346},
keywords = {graph summarization, graph visualization, matrix-based visualization, matrix reordering},
url = {https://www.sciopen.com/article/10.1007/s11390-025-5275-5},
doi = {10.1007/s11390-025-5275-5},
abstract = {Matrix-based graph visualization is effective in revealing relationships among entities in graphs. The visibility of structural patterns depends on the ordering of rows/columns in matrices. Most existing approaches mainly settle on an ideal ordering according to quality metrics, which emphasize certain types of patterns but ignore others. This paper proposes a summarization-based pattern-aware reordering approach to highlight multiple patterns simultaneously. First, the pattern-aware graph summarization utilizes the Minimum Description Length (MDL) technique to identify various types of patterns from the input graph. Second, we propose a coarse-to-fine reordering mechanism to generate matrix-based visualizations that maintain the structure of all identified patterns. Experimental results of two comparative studies and a user study on several datasets demonstrate that our approach simultaneously highlights more types of patterns than other approaches and performs well across multiple quality metrics.}
}