# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 5 Use Cases
### Dynamic Edge Bundling Visualization  
In the second use case, we show how to use SDEB on the analysis of dynamic graphs. In this example, we create the **#NBABallot** dataset. The **#NBABallot** consists of a collection of posts extracted from Twitter related to the American _National Basketball (NBA)_ league. To compose this dataset, we selected tweets published with the _hashtag_ #NBABallot between 19 December 2014, and 21 January 2015. This _hashtag_ was used to vote in a player to compose one of the two teams of the _2015 NBA All-Star Game_. This election selected five players for each team, the _Eastern All-Stars_ and the _Western All-Stars_. During this period, more than 2 million posts (votes) were collected. After collecting the tweets, we processed them to create our dataset. In the resulting graph, vertices represent players, and the data used to calculate the similarity among them was extracted from the official league statistics page ([[http://stats.nba.com/](http://stats.nba.com/)]([http://stats.nba.com/](http://stats.nba.com/))), which covers the major statistics measures of basketball matches (e.g., points, rebounds, assists, turnovers, field goal percentage, three-point percentage, blocks, and others). Edges connect players that had received votes from the same user. This graph was segmented by tweets days, which we call a _voting frame_. The final graph consists of a dynamic graph with 436 vertices, 11,793 distinct edges, and 34 frames. Considering the whole list of edges, we found 565,829 associations between pairs of players who received a vote from users on the same day. Before examining the resulting graph, we start our analysis explaining the _backbone_ showed in Figure 13. Just to set important terms for the reader nonfamiliar with basketball, players are commonly assigned to one of five positions in the court, which are _Point-guard_, _Small-guard_, _Small-forward_, _Power-forward_, and _Center_. The first two positions are usually called _guards_, while the last two are usually called _posts_. _Small-forward_ players are versatile players that may be associated with both _post_ and _guard_ positions, according to the team strategy. In this figure, some groups are highlighted to identify leading players. The first noticeable pattern is the first division in the _backbone_. It splits the players into two major groups. The first group (on the right) represents the more active players (i.e., starters or engaged bench players), while the second group (on the left) is formed by less active players (i.e., players that usually play for a few minutes). The right side concentrates the most famous athletes in the league (high on many statistics). On the contrary, players from the left side are less prominent. Figure 12: Edge bundling layout summarization by collapsing _backbone_ branches. A circle pack representation contains the most frequent terms found in four different groups of papers showed in the auxiliary list on the right. The color of each entry identifies the group each paper belongs to. After the second _backbone_ division, it becomes harder to analyze the left side since players with a few minutes per game have inaccurate or incomplete statistics. Regarding the right side, the _backbone_ clearly separates _post_ players, placed at the top, and _guard_ players, placed at the bottom. The _backbone_ shows more similar players on deeper levels. The red group encompasses the ones considered to be the best players in the league. These players are known to have good performances in most statistics, making them the leading players of their teams. The blue group identifies players with similar abilities, but they do not have a leading role. Groups identified in yellow are similar to the red and blue groups. The players from the yellow groups are important. Still, they present good performance in only a few metrics, making it evident that it is possible to classify players according to their position on the court only using their statistics.