## 1 Introduction  
In recent times, global collaboration between researchers has majorly expanded the level of cooperation between them and universities with regard to the exchange of knowledge and resources. Academics are now transmitting their knowledge and publications throughout the world. Digital technology has the power to bridge distances and promote cross-disciplinary and cross-border collaborations, which are an inspiration for scholarly networks. Such collaborations will consequently improve the experience of social activities at conferences and will create larger professional networks. Scholarly networks also help with shared learning and the transfer of technologies to poor countries. Such partnerships between scientists increase the number of citations and open up new and improved opportunities for further research and collaboration. Today, the transfer of knowledge has become extremely easy, and the number of citations has increased tremendously [1]. In addition, scholarly communities have revolutionized the methods of information publication and research sharing. Indeed, many of these groups have emerged and created interrelations that may affect the structure of the research community itself. For instance, the website ResearchGate is designed to facilitate access to academic research and increase collaboration between researchers. People from all around the world register there and obtain an instant online presence, and it also provides them with a convenient platform for showcasing their research, even before the publication stage. Today, researchers commonly use such platforms to discuss their research with the academic community and create professional partnerships. One of the largest examples is Google Scholar, which supports new and inexperienced scientists in finding support and advice from people who are more experienced in their field. Despite this easy access, scholarly communication and publishing still face shortcomings in terms of maximum accessibility and usability, support for an expandable range of contributions, open infrastructure, and, most importantly, community building [2]. In recent years, social-network (SN) analysis has become a widely used technique across diverse fields. SN analysis is not only applied to analyze social media applications, such as Facebook and Twitter, but it is also more broadly utilized in relation to scientific research, where it provides integrated services. Moreover, this form of analysis has been increasingly used to aid research on subjects by facilitating socialization and collaboration as SNs bring together interrelated organizations and individuals with common interests or objectives. Academic social networks (ASNs) contain academic entities, such as publications, authors, academic institutions, keywords, or content, which are represented by nodes (Figure 1), whereas connections between entities usually indicate relationships such as co-citations and co-collaboration. These different types of relationships form different networks, which create diverse perspectives in terms of scholarly communication and research interaction. For researchers with a vast amount of scholarly data, extracting valuable collaborations is often a highly time-consuming and complex task. Through ASNs, looking deeper into scholarly relationships may aid scholars in determining possible co-authorship. Finding collaborators for researchers is a critical problem that needs to be addressed; therefore, a need exists to address relevant k-collaborators for a selected author [3]. Furthermore, scholarly data is expanding every day, creating a knowledge repository. This heterogeneous network contains various types of information, such as papers, journals, and authors. At present, the extraction of relevant experts in a specified field is one of  
Figure 1: Academic social networks’ entities [3]. the most critical research topics, and experts are considered the most crucial entity in the vast knowledge society.