A grammar-based layout method for graph models

Yufeng Liu; Yangchen Zhou; Fan Yang; Song Li

doi:10.1117/12.3014367

9 January 2024 A grammar-based layout method for graph models

Yufeng Liu, Yangchen Zhou, Fan Yang, Song Li

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296917 (2024) https://doi.org/10.1117/12.3014367
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Graph model layout technology is an important cornerstone in graph visualization. Although the present graph model layout methods have been well studied, there are obvious problems: (1) excessively high initial state correlation; (2) excessive reliance on local optimal solutions; (3) limitation on the number of nodes. In this paper, we propose a new graph layout method on a graph grammar framework. First, the input graph model is parsed by graph grammar, with the reduction process recorded. Next, in the reverse order of reduction, the derivation operation starts from the initial graph and ends at a redrawn graph, with a new layout that meets the required specifications. Compared with other methods, regardless of the initial state, this method combines global and local layout specifications in productions and provides an intuitive yet effective way for the graph layout adjustment.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yufeng Liu, Yangchen Zhou, Fan Yang, and Song Li "A grammar-based layout method for graph models", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296917 (9 January 2024); https://doi.org/10.1117/12.3014367

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