## 1 Introduction
Indeed, in many scenarios, the recognition of researchers' skills is of considerable importance; for example, appreciating the qualitative attributes of an expert can be vital to a project. In every field of life, experts with professional skills, knowledge, and experience are required. From social institutions to research institutes, they play an integral role in team guidance, team management, and productivity and efficiency improvements. Experts are diverse in nature and continually evolve; thus, acquiring relevant experts from a wide range of information networks is critical. In some instances, authors have similar areas of expertise but differ in their specialized fields. For instance, many papers have been written on computer science; identifying an author who specializes in computer science is crucial because he or she could be a specialist in artificial intelligence, networks, web development, or data science. Similarly, this logic can be applied to all other domains of science, technology, the humanities, and anywhere research work is applied [4]. However, to keep track of the expertise in an academic community, researchers are diligently trying to automate this problem within this domain. By processing the content of published research papers, several theories have been proposed to extract the expertise of authors. We have reviewed the existing approaches in [5], where a few have focused on query matching using keywords [6], while others have focused on the number of citations [7] or extracting and categorizing scholarly articles using their abstracts [8]. On the other hand, some research has concentrated on mining the relationships of authors with the aim of finding the most suitable collaboration based on the similarities between them; for example, studies have inspected likenesses in terms of venues and topics [9], co-word and co-citation similarity [10], the topics used and the academic rank of collaborators [11], or the demographical data of their topics [12]. Consequently, information visualization is a valuable methodology for organizing data with higher volumes and providing accurate computations, especially for a scholarly network. However, this field is still progressing, and researchers are attempting to make the task more efficient, less time-consuming, and less complex. The overall aim of this research is to build a model for academic researchers to recommend the most significantly influential collaborations based on similarities in their publication records while recognizing the most significant factor with regard to the outcome similarity patterns. Additionally, another goal is to identify the expert authors who have a significant role in each research area and to facilitate the representation of this information into a network graph by grouping them together based on their research areas. Furthermore, studying the influence of collaborations with universities where these institutions are affiliated with authors' publications is also a priority. In particular, we examine the following research questions:  
* Q1: How do the selected factors affect the quality of the collaboration recommendation? * Q2: How can the  
* Interactive network representation of the predicted authors' collaboration: utilizing a topic-clustering model based on their main research area, and for each author, there is a personalized ranked list of the top k-potential collaborators. * Building a dataset: includes the multi-factors of individual authors' publications in order to mine their academic research area. The rest of the paper is organized as follows. Section 2 begins with definitions of the main concepts discussed in this research and then goes on to review related studies. Section 3 elaborates on the methods and materials used in this research, while Section 4 discusses the experiments along with their results; a discussion of these results will be presented in Section 5. Finally, Section 6 concludes the paper with a summary.