Graph Visualization: Alternative Models Inspired by Bioinformatics

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 7; p. 3747
• Main Authors: Kolomeets, Maxim, Desnitsky, Vasily, Kotenko, Igor, Chechulin, Andrey
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.04.2023; MDPI
• Subjects: Analysis; Bioinformatics; data visualization; Graph representations; human–computer interaction; node-link diagrams; Research methodology; Review; Scientific visualization; Software utilities; Visualization; Visualization (Computers); visualization models; Germany; visualization models; node-link diagrams; data visualization; human–computer interaction; review
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23073747

Currently, the methods and means of human-machine interaction and visualization as its integral part are being increasingly developed. In various fields of scientific knowledge and technology, there is a need to find and select the most effective visualization models for various types of data, as well as to develop automation tools for the process of choosing the best visualization model for a specific case. There are many data visualization tools in various application fields, but at the same time, the main difficulty lies in presenting data of an interconnected (node-link) structure, i.e., networks. Typically, a lot of software means use graphs as the most straightforward and versatile models. To facilitate visual analysis, researchers are developing ways to arrange graph elements to make comparing, searching, and navigating data easier. However, in addition to graphs, there are many other visualization models that are less versatile but have the potential to expand the capabilities of the analyst and provide alternative solutions. In this work, we collected a variety of visualization models, which we call alternative models, to demonstrate how different concepts of information representation can be realized. We believe that adapting these models to improve the means of human-machine interaction will help analysts make significant progress in solving the problems researchers face when working with graphs.