Towards a Hybrid Layout for Complex Networks Visualization

The complex networks importance within Bioinformatics research has grown at a similar rate with biological data growth. Making sense of such a large data amount requires diverse tools to help with visual representation. Complex networks represented as graphs are a common approach to extracting intrinsic information out of the studied data. However, as underlying data grows in quantity, networks become more crowded and as a consequence, regular means of visualizing them start falling behind and become less efficient. This category of tools have multiple directions of improvement - either speed and efficiency, or eye-catching UI yet less performance. The results produced by them consist of 2D or 3D layouts, however, none of them convey a real sense of 3D, spatial correlation or stable positioning within a fixed reference system. This paper proposes a new hybrid approach (2D & 3D) to networks visualization, which aims to solve the 3D problem in a gridded manner, with clear reference points and by decluttering dense networks. All these could dramatically improve researchers' understanding of studied data.