## 4 Experiment and Results
### Results  
This research adopted different evaluation metrics to assess the quality of the inferred topics and the proposed matrix. This section describes the two different evaluation methodologies: ground-truth evaluation and quantitative analysis. Hence, the authors have been clustered based on their topics, and the evaluation of the clustering performance was completed in terms of precision, recall, F1-measure, accuracy, and Cohen's kappa. The cluster labels of the topics were obtained from the model along with their original labels from the ground-truth table using a binary representation. Table 15 shows the overall assessment statistics, while Figure 24 is a heatmap representation of the results. Figure 23: Universities’ interactive co-collaboration network. Figure 24: Confusion-matrix evaluation results. Based on these results, the proposed model delivers significantly higher levels of accuracy when predicting the actual topic and therefore correctly assigns the authors' topics that are related to each other in the ground-truth table. Additionally, quantitative analysis was conducted, which equated to the evaluation process of the proposed advanced similarity measure and the robustness of the results; the aim of this was to discover the influence of the selected factors on the similarity results' accuracy, which was conducted by selecting different cases from the obtained data and applying the proposed approach. Three factors will be assessed in order to evaluate the validity of the claim made in this research: authors' keywords, publication statistics, and topic distribution. As a result, this qualitative analysis covers all applicable cases, which include the following instances:  
* Case 1: Slightly differ in terms of keywords, similar with regard to publication statistics, and highly similar in relation to topic distribution. * Case 2: Slightly differ in terms of keywords, similar with regard to publication statistics (theoretically highly different), and highly similar in relation to topic distribution. * Case 3: Differ in terms of keywords, similar with regard to publication statistics, and highly different with regard to topic distribution. * Case 4: Highly different in terms of keywords, similar with regard to publication statistics, and highly different in relation to topic distribution. Moreover, expertise extraction analysis was employed, which is the evaluation process of the proposed metric to extract the expertise evidence in order to employ expertise identifications, allowing us to assign the experts. We completed this by selecting randomly different cases from the obtained data and applying the proposed approach. First, we converted the weighted topics' distribution for each paper into vector format by considering only the highest topic weight as the main topic for the paper and replacing it with 1 and 0 for all of the other topics' weights. We then added the corresponding citation counts for each paper to their identified topic. For each author in each topic, we counted the number of identified papers and added them to their corresponding citations; in addition, the averaged topic weight was included, which had previously been extracted. Table 16 shows the selected factors for each author where for each topic there are three factors to be computed for instance. Papers 0 represents the number of papers in Topic 0; Citations 0 is their corresponding citation count for papers in topic 0; Topic Weight 0 is the averaged weight for topic 0 publications distribution; and Expertise 0 is the average weight for topic 0, which denotes the number relating to the author's expertise with regard to Topic 0 based on his publications. After ranking the resulting expertise for each topic, the author with the highest weight was considered to be the expert in that area. Table 17 highlights the top-three academics with the most experience in each topic.