## 4 Experiment and Results
### Experiment Settings  
This section provides a detailed description of the experiments that were carried out in order to meet the research objectives, including how they were setup and what actions were taken. This research was performed using a MacBook Pro that was running macOS Big Sur 11.2.3 while using Jupiter Notebook from Anaconda Navigator with Python 3.9. Furthermore, all of the researchers' contributions to science are set out in the publication lists. This study focused on individual authors' publication activity to find similar peers who may be suitable for future collaborations, and three experiments based on the authors' publications were explored with regard to the authors' similarities, their levels of expertise, and constructing the recommended co-collaboration network.  
E1-Authors Similarity: The main objective of this experiment was to find the similarities between the authors based on three factors. First, this was completed by finding the similarities based on the keywords used in their publications. Second, their publication records statistics were utilized, which consist of the number of their published pieces of work, how many citations they have, and the range of years where they had papers published. Third, an extraction of the topics they worked on using their keywords was undertaken, meaning the topic of each of the author's publications could be determined. The topic distribution was then aggregated from all of the author's publications by taking the average weight of each topic in order to find the similarities based on the topics' averaged weight. Finally, the similarities between the authors were computed by combining all of the resulting similarities.  
E2-Assessing Authors' Expertise: This experiment aimed to determine the expert author for each research area by considering three factors for each topic: the number of published papers on the topic; the citations count; and the topic weight, which had extracted from the previous experiment. The topic distribution for each paper was subsequently transformed into a vector format, which involved highlighting the main topic for each paper alongside their corresponding citation count. Then, this was also completed in  
Figure 14: Co-occurrence keywords’ network.  
relation to each author's topic aggregate with regard to all of their papers alongside their corresponding citation count. After that, for each author, the weighted average including the three factors was computed. Therefore, the authors were ranked in terms of each topic, meaning that those individuals with the highest value became the expert in that area.  
E3-Constructing the Network: The objective of this experiment was to build an interactive network that represents the recommended co-collaboration between authors based on their similarity from the first experiment. Hence, the first step was to extract the main research area for each author, and the average weight for each topic was taken from the second experiment. Each author's topic with the highest score was considered to be their main research area. The academics were then clustered according to their main topic. A network was subsequently built using the authors as nodes and the similarity values as the edges. Finally, further interactive features were added to the network.