# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 4 Results and Evaluation
### Qualitative Analysis
#### 4.2.1 Bundling Evaluation  
We compare the _Similarity-driven Edge Bundling (SDEB)_ technique with CUBu, FDEB, and MINGLE considering both the radial and the swapped H-Tree layouts. Since these techniques rely on vertices' positioning, we use the same vertices placement given by our backbone for all layouts. For all techniques, we determined the best possible parameters for such input graphs. For the SDEB, we set a filter of intermediate vertices to drop the first two levels of the STree (more details in Section 4.2.2). The comparisons considering the radial layout are presented in Figure 6.  
For all graphs, we noticed that FDEB and MINGLE fail to present well-defined bundles. The worst results were presented by FDEB, which could not create any meaningful bundle, resulting in an entirely disorganized layout. MINGLE creates bundles with a few edges. However, the layout is heavily cluttered in the border of the visualization. Moreover, FDEB and MINGLE present several \"outlier\" edges, i.e., edges that are not grouped into any bundle. CUBu presents less cluttered layouts. The expected dense connections between groups are recognizable, as shown in the adjacency matrix presented in Figure 5. However, this technique generates bundles leaving the center of the visualization empty and the border crowded with too much information. Different from CUBu, SDEB does not agglomerate the edges around the border of the visualization. Instead, our technique follows the backbone, which distributes the bundles making better use of the visual space. Most crossings in the SDEB layout happen in different directions, reducing ambiguities resulted from edges crossings. Figure 7 shows in detail the differences between CUBu and SDEB. CUBu bundles all edges close to their source/target vertices, resulting in misleading bundles. SDEB draws edges following a direction perpendicular to the radial layout's tangent, with the bundling happening at different levels. Besides, CUBu seems to mix several bundles in a way that disregards  
Figure 5: Scatterplots of the vertices positions and adjacency matrix visualizations of the graphs’ edges patterns used in the qualitative evaluation. Three different sets of vertices presenting different distance relationships are combine with two different edges distributions resulting in six different graphs. bundles' meaning. On the contrary, SDEB defines bundles in which it is possible to identify the groups of vertices represented in each bundling. Figure 6: A comparison among the results attained by the SDEB, CUBu, FDEB, and MINGLE using the radial layout. FDEB fails to present well-defined bundles. MINGLE creates bundles with a few edges, but the layout is heavily cluttered in the visualization border. CUBu presents a similar problem, leaving the center empty and the border crowded with too much information. SDEB does not agglomerate the edges around the border while also makes better use of the visual space. Most crossings in the SDEB layout happen in different directions, reducing ambiguities resulted from edges crossings. Figure 8 presents the resulting bundling comparing the same techniques but using the swapped H-Tree layout to position the vertices. As explained in Section 3.2.1, the H-tree layout focuses on the vertices, spreading them through the visual space. Even though the techniques share the same vertices position, the difference in bundling readability is evident. This happens because CUBu, FDEB, and MINGLE generate the bundles only considering spatially-similar edges trajectories, while our technique follows the _backbone_, which is mapped to the plane using the H-Tree technique. Our approach takes advantage of the H-Tree layout to produce more compact bundles and a less cluttered representation than any other technique. Also, vertices placed in the center of the visualization are easier identified. Although there is some overlapping of edges over vertices, this occurs less often than in the other techniques, in which the bundles completely hide centralized vertices.