# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 5 Use Cases
### Edge Bundling Simplification  
In the first use case, we use a subset of the _Visualization Publication Dataset_ (**infovis**) [44] to show how SDEB can be used to explore graphs with different levels details. This subset is composed of papers with at least five citations, resulting in 384 papers (vertices) and 1633 cross citations (edges). We calculate the similarity between papers using a bag-of-words representation of their content (abstract, title, and authors list) and the cosine dissimilarity. Figure 11 shows the visualization using  
Figure 10: Multiples bundling configurations varying the interval of intermediate filtering. Removing intermediate vertices closer to the root creates a more detailed visualization while dropping intermediate vertices closer to the leaves generates bigger and more generic groups.  
Figure 9: Multiples bundling configurations varying the edge tension parameters \(\gamma\) and \(\Delta\). By properly setting up these parameters, hidden local information can be revealed separating small edges from the main bundles.  
the radial layout. We set the parameters \(\beta=0.97\), \(\gamma=0.1\), \(\Delta=0.12\), and the filtering function of control points to \(\phi_{L}=2\) and \(\phi_{U}=7\). Edges' colors represent their sizes.  
With these parameters, small edges are less bent towards the _backbone_, and interesting patterns emerge, for example, the high number of short edges. This is expected since similar papers are close in the radial layout, and those papers should present similar main subjects and, therefore, the same (circular) references. However, the distribution is not regular, indicating that some papers or topics concentrate more citations among related papers, while others cite the same papers less frequently. Another interpretation is that, recapping that this dataset only contains papers of the _IEEE Visualization_ conference, some topics are more self-contained in the visualization community while others have references outside the field.  
In this visualization, inter-group relationships can also be observed. However, given the number of vertices and connections, it may be time-consuming to make conclusions. To alleviate this problem, the _backbone_ hierarchical structure can be used to summarize the graph, allowing a details on-demand approach to be employed. By applying the multi-level control in certain _backbone_ branches, the user can select groups of similar vertices and visualize them as a single vertex. In the summarized visualization, the selection hides the grouped vertices and edges and shows the group's information. Figure 12 presents the summarization of four groups, each one identified by a different color with the content of the hidden vertices depicted by a circle pack representation with the most frequent terms contained in the grouped scientific papers. On the right side, a list of the selected papers is displayed.  
Figure 11: Edge bundling layout from the **infovis** dataset. Non-bundled short edges indicate, as expected, that similar papers cite the same papers. However, this is not uniform across the different groups of papers (or topics).