## 5 Discussion of the Results  
The use of academic search engines, such as Google Scholar, is increasing. It is therefore becoming more important for authors to identify well-ranked papers as well as authors with similar research interests. Additionally, the use of network representation has proven its remarkable functionality by facilitating and accelerating the identification of comparable peers. The main aim of the network in this research was to assess and extract an author's expertise in order to direct them towards a highly recommended collaboration. Furthermore, to identify similar authors in the most efficient manner using academic search engines, this research concentrated on identifying similar authors using three key factors: the author-selected keywords, publications statistics, and the topics of the publications. The extracted main topics were evaluated with an accuracy of 88.4%. However, when evaluating the resulting similarity with selected cases in the previous section, it was indicated that the greatest priority out of these factors is the publication topic distribution, which dominated the accuracy of the results. As shown in Table 18, Case 1 had slightly different keywords, but had a high statistical similarity with other publications, even though they are theoretically slightly different. Therefore, the topic similarity has finalized the result with accurate similarity, while neglecting the statistical publications' dissimilarity. Additionally, since they have a high level of topics' distribution similarity, they also share the same main research area, which was extracted from the topic distribution. This confirms that the topic extraction was very accurate. On the other hand, Case 2 has a high rate of variance in relation to other publications' statistics in spite of being recognized as a similar as topic distribution, which has standardized the results again into meaningful similarity. Case 3 differs in terms of keywords and theoretically differs slightly with regard to its similarities with other publications, as the topic distribution here was very different, which indicates that they are dissimilar authors in relation to their publication areas. Consequently, they also differ in terms of their main research area. Meanwhile, Case 4 contains an important observation regarding the topic distribution, which represents the weight of each topic according to the authors' publication: scholars may have knowledge of a topic but might not be an expert in it. Therefore, the topic distribution gives an accurate assessment of the results. In this case, even when the authors are dissimilar in two respects but are similar in some topics, they may still collaborate and should not be considered totally dissimilar. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Factors** & **Topic 0** & **Topic 1** & **Topic 2** & **Topic 3** & **Topic 4** & **Topic 5** \\ \hline \multirow{4}{*}{Author 1} & Papers & 0 & 1 & 5 & 14 & 21 & 1 \\  & Citations & 0 & 3 & 17 & 95 & 76 & 8 \\  & Topic Weight & 0.08907328 & 0.09769872 & 0.6399102 & 1.12391406 & 2.61450467 & 0.25997465 \\  & Expertise & 0.053444 & 0.858619 & 4.783946 & 22.474348 & 20.968703 & 1.955985 \\ \hline \multirow{4}{*}{Author 2} & Papers & 19 & 8 & 17 & 3 & 14 & 0 \\  & Citations & 345 & 31 & 412 & 22 & 418 & 0 \\  & Topic Weight & 2.69323321 & 1.48435841 & 1.92540924 & 0.57017606 & 4.03028581 & 0.33310417 \\ \cline{1-1}  & Expertise & 74.4159399 & 8.69061505 & 86.9552455 & 5.34210563 & 88.2181715 & 0.1998625 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Process of extracting the authors’ expertise.