# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 6 Discussions and Limitations  
As formalized at the beginning of Section 3, SDEB assumes that each graph node represents data objects described through numerical values. That is, the vertices represent objects embedded in a vector space. One reason is related to the very own technique's nature, which is to guide the bundling using data information, in our case, distances between objects. The second is associated with the procedure we develop to create the _backbone_ (Section 3.1). In this procedure, the function \(\textsc{Centroid}(C)\) returns the mean of a group of data objects, and this assumes objects embedded into a (multidimensional) vector space. This limitation can be addressed using the medoid of a group of objects, the object whose average dissimilarity to all the group objects is minimal, instead of its centroid. This opens some interesting possibilities. For instance, the adjacency (edges) information could be used to compute the similarity between vertices (see [3]), resulting in bundles with a different semantical meaning. A second limitation of our technique is the assumption that the vertices' position preserves distance relationships (of the data objects) and is guided by the _backbone_. So for graphs in which the vertices positions are pre-defined, for instance, geographical location, SDEB cannot be employed. We tried to use the defined vertices positions to calculate the distances when creating the _backbone_ to address such a limitation. However, the results were unsatisfactory (too many crossings between _backbone_ paths), and using positions to generate the _backbone_ instead of data information violates our design principles and the primary goal of bundling edges according to the semantic defined by data objects/vertices. In this paper, the produced layouts were informally evaluated during their design phase using a think-a-loud process, where participants explain what they are seeing while exploring different layouts. The results were encouraging, especially for the more common circular representations, and for the H-Tree, up to a certain extent. However, these informal tests cannot be considered evidence of the proposed approach or edge bundling layouts efficacy. Despite that, they resulted in one interesting observation. The H-Tree symmetrical layouts are not as visually pleasing as the circular layouts, although this does not influence the overview interpretation of a graph. One potential solution could be running a force-directed algorithm using the swap H-Tree as the initial layout, reducing the fractal appearance but incurring possible overlaps. All in all, user evaluation is still a bottleneck, not only for our approach but also, to the best of our knowledge, to the entire field of edge bundling techniques. And a complete evaluation of bundling efficacy remains as future work and a challenge. Although SDEB is slower than the state-of-the-art, it does not demand any special hardware to be executed, such as GPUs, being very simple to implement and execute. Also, SDEB offers a data-driven multilevel interpretation of bundling layouts, something not provided by the fastest techniques. If this  
Figure 18: Original and CUBu layouts of the **amazon** dataset. Both layouts cannot organize edges into meaningful bundles and vertices are hidden, especially in dense areas of edges, impacting the analysis and insights that can be derived. is more important for a given analytical scenario, the drop in running time can be acceptable. However, if speed is mandatory, SDEB is not the best choice. Notice that our implementation is a mix of java and javascript codes, and the running times can be dramatically decreased using a GPU-based implementation. Also, observe that we have two phases in our approach (Figure 1) and the _Graph Drawing_ phase, where user interaction happens (changing \(\gamma\) and \(\beta\) parameters), is independent of the _Backbone Construction_.