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.

Traditional Chinese painting (TCP) is an invaluable cultural heritage resource and a unique visual art style. In recent years, there has been a growing emphasis on the digitalization of TCP for cultural preservation and revitalization. The resulting digital copies have enabled the advancement of computational methods for a structured and systematic understanding of TCP. To explore this topic, we conduct an in-depth analysis of 94 pieces of literature. We examine the current use of computer technologies on TCP from three perspectives, based on numerous conversations with specialists. First, in light of the “Six Principles of Painting” theory, we categorize the articles according to their research focus on artistic elements. Second, we create a four-stage framework to illustrate the purposes of TCP applications. Third, we summarize the popular computational techniques applied to TCP. This work also provides insights into potential applications and prospects, with professional opinion.