Abstract
Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, edge bundling has attained relative success in improving node-link layouts by bending and aggregating edges. Despite their success, most approaches perform the bundling based only on visual space information. There is no explicit connection between the produced bundled visual representation and the underlying data (edges or vertices attributes). In this paper, we present a novel edge bundling technique, called Similarity-Driven Edge Bundling (SDEB), to address this issue. Our method creates a similarity hierarchy based on a multilevel partition of the data, grouping edges considering the similarity between nodes to guide the bundling. The novel features introduced by SDEB are explored in different application scenarios, from dynamic graph visualization to multilevel exploration. Our results attest that SDEB produces layouts that consistently follow the similarity relationships found in the graph data, resulting in semantically richer presentations that are less cluttered than the state-of-the-art.
Highlights
Graph visualization has been applied in a wide range of domains to model and support the analysis of different relationships between elements, from protein-protein interaction [1] and biomolecular relationships [2] to social networks [3]
We present Similarity-Based Edge Bundling (SBED), an adaptation of the Hierarchical Edge Bundling (HEB)
To evaluate the quality of trees produces by the Similarity Tree (STree) algorithm, we compare it with the Neighbour Joining (NJ) [34] and the UPGMA hierarchical clustering [32] since both present the same goal of distance preservation
Summary
Graph visualization has been applied in a wide range of domains to model and support the analysis of different relationships between elements, from protein-protein interaction [1] and biomolecular relationships [2] to social networks [3]. Effective graph visualization presents several challenges, especially when dealing with dense graphs with many vertices or edges [4,5] In these scenarios, the visualizations usually present considerable overlapping of visual elements, resulting in cluttered layouts that make it hard or even impossible for users to extract relevant information. We present Similarity-Based Edge Bundling (SBED), an adaptation of the HEB technique to construct bundling layouts that do not depend on an external hierarchy and consider similarity relationships among the vertices to add semantics to the bundled representations.
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