# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 2 Related Work
However, as the geometric-based approaches, they only consider the visual space's information to perform the bundling, therefore producing layouts that ignore the underlying information contained on the graphs (vertices attributes) to create the bundles. The Multilevel Agglomerative Edge Bundling (MINGLE) [15] employs an alternative approach to promote the bundling. MINGLE aggregate the edges based on an ink-minimization concept. The proposed algorithm iteratively groups the nearest edges until the amount of ink used to create the visual representation reaches a minimum. This process can be viewed as an edge-simplification strategy, where groups of edges are aggregated and represented as single edges. After the simplification, curved lines connect these aggregated edges to the original vertices. Although a de-cluttered representation can be produced by reducing the number of lines representing edges, the resulting layouts are usually less readable due to the lack of a clear definition of the main edge patterns compared to the previous techniques [19]. Although the previous methods produce good results regarding running times, allowing the real-time processing of thousands of edges, the layout meaningfulness is somewhat neglected when processing graphs with information on the edges or vertices (data attributes). This information is not used in the bundling process, so there may be no connection between the visual representation and the underlying data. Recently, a few techniques define strategies where some of this information is considered. The Attribute-Driven Edge Bundling (ADEB) [20] extends the KDEEB by using edge attributes to set the bundling flow map, a feature likewise supported by the FFTEB technique. Also, the FDEB technique has been extended to include semantic properties inherent to edges [29], edge type, or attributes [22] to compose compatibility measures [30] on the force model calculation. Only edge information is considered in these cases, while the vertices' information is still ignored. In contrast, our approach uses vertex information to guide the bundling process, identifying and displaying similarity-based patterns on graph data. We preserve the graph topology on the produced layouts but re-arrange the nodes' positions using a structure that captures similarities with high precision. Our technique adds semantics to the visual representation by focusing on similarities, supporting different analytical scenarios not covered by the existing techniques. Finally, the goal of existing bundling techniques is, in general, to simplify the visual representation of a graph, emphasizing the main topological patterns presenting on the entire data set. Thus, an inherent visual scalability limitation is common to all techniques, especially when handling large or dense graphs due to the information overload imposed on the user. Our approach address this scalability problem supporting a multiscale bundling representation. Our approach is one of the first to allow multiscale bundling analysis, enabling the graph exploration on different levels of detail from coarser levels, better suited to explore the main patterns between groups, to finer levels to verify intra-group relationships.