## 4 Experiment and Results
### Generating Interactive Visual Networks
#### 4.5.1 Authors' Interactive Network  
The authors' collaboration network is one of the important networks that are used to visualize and evaluate the implicit relationships that need to be uncovered. This research focuses on finding the most similar authors based on their publications in order to generate a list of valuable possible collaborators for the target author. The network is therefore based on the similarities that have been computed, the extraction of the authors' main research areas and, their expertise. Network Construction: The underlying idea of building the co-collaboration network is that authors can be clustered together based on their main research areas while identifying the expert. Link Weight Calculation: This is based on the selected factors adding a link between the authors, while its weight reflects their corresponding similarities. Consequently, the authors were clustered based on their main research area, and one author in each cluster was assigned as the role of expert (represented by a larger node size). All of the nodes represent authors, while the edges (links) are the predicted collaboration based on their similarities. Figure 19 visually depicts the collaboration network of authors without constraining the edges with a threshold; hence, the network is very dense and can be pruned by setting the threshold and removing irrelevant edges. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Topic** & **Authors** & **Papers** & **Citations** & \begin{tabular}{c} **Topic** \\ **Weight** \\ \end{tabular} & **Expertise** \\ \hline Topic 0 & \begin{tabular}{c} Author 1 \\ Author 2 \\ Author 3 \\ \end{tabular} & \begin{tabular}{c} 21 \\ 7 \\ 19 \\ \end{tabular} & \begin{tabular}{c} 282 \\ 517 \\ 263 \\ \end{tabular} & \begin{tabular}{c} 2.65666089 \\ 3.38440497 \\ \end{tabular} & \begin{tabular}{c} 62.19399653 \\ 105.337527 \\ 58.43064298 \\ \end{tabular} \\ \hline Topic 1 & \begin{tabular}{c} Author 1 \\ Author 2 \\ \end{tabular} & \begin{tabular}{c} 45 \\ 29 \\ \end{tabular} & \begin{tabular}{c} 927 \\ 1009 \\ \end{tabular} & \
\begin{tabular}{c} 5.84844025 \\ 4.03656339 \\ \end{tabular} &
\begin{tabular}{c} 197.9090642 \\ 210.021938 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Extracting authors’ expertise. Figure 20 represents a sample of the interactive network's functionality where the nodes represent the authors, and the edges are their similarity percentage. If a node is clicked on (or hovered over), all of the connected nodes will be highlighted, and the features window will be displayed. The features consist of the name of the author with their number of papers and citations as well as the range of years they have been active, their main research area, and their corresponding affiliation. Moreover, this information is accompanied by a ranked list of connected authors based on their similarities. Although it is not possible to unambiguously assign an author to another author, the ranked list contains the similarity levels between the scholars. In order to only present the significant similarities between the authors, a threshold was set for the edge value; to be more specific, a network hyperlink between two nodes denotes their cooperation relationship. Discovering those nodes with high values is preferable when evaluating the magnitude of the predicted relationship, so all of the insignificant similarity values are ignored, as no edges lower than 67% will be added. Figure 21 demonstrates the final representation for the authors' interactive network where the colors of the nodes represent the authors' main topics (Topics names presented in Figure 19). Table 13 illustrates the network entities' descriptions. Moreover, the PageRank algorithm was employed to extract the author with the highest number of similarities. The author with the highest degree of centrality is considered to be an influential member of the group, and they are represented by the node with the highest number of collaborations with other network members.