Abstract
We present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology. Clustered graphs are often used to model relationships that are accompanied by attribute-based grouping information. Such information is essential for robust data analysis, such as for the study of biological taxonomies or educational backgrounds. Hence, the ability to smartly arrange textual labels and packing graphs within a certain screen space is therefore desired to successfully convey the attribute data . Here we propose to hierarchically partition the input screen space using Voronoi tessellations in multiple levels of detail. In our method, the position of textual labels is guided by the blending of constrained forces and the forces derived from centroidal Voronoi cells. The proposed algorithm considers three main factors: (1) area balancing, (2) schematized space partitioning, and (3) hairball management. We primarily focus on area balancing, which aims to allocate a uniform area for each textual label in the diagram. We achieve this by first untangling a general graph to a clustered graph through textual label duplication, and then coupling with spanning-tree-like visual integration. We illustrate the feasibility of our approach with examples and then evaluate our method by comparing it with well-known conventional approaches and collecting feedback from domain experts.
Highlights
We present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology
Life functions are organized in a relationship of interacting elements and chemical compounds that form a supercomplex network of reactions occurring throughout the entire life form
To semantically organize these enormous networks, they can be segmented into network sub-elements, known as pathways, to form a graph containing dozens of chemical elements that represent a particular function of life
Summary
VER recent decades, graphs have been developed to formulate relationship networks between entities. Based on hand-made graphs and prior experience of box-based clustered graph visualization [64], this approach supports more free-form organic shapes of graphs to ensure the high effectiveness of the layout compaction This is achieved by a four-level area balancing approach to allocate appropriate space for each vertext within a cluster. Each level is computed by a force-based layout (see Section 4) followed by a schematization approach for simplifying the shapes of the contours (see Section 5) to accomplish detail-level vertext area balancing. It consists of elements mGC ∈ MGC that are used as a representative unit for a certain number of detail-level vertexts within a cluster (Figure 1(b)) This is done by replacing the representative vertex for a cluster with a cycle graph, which enables the flexibility of vertices in the cycle graphs to move during the layout process. Such a map metaphor has proved its usability in previous approaches [25], [31]
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