On the Co-authorship network analysis in the Process Mining research Community: A social network analysis perspective

As a noticeable focal point in the field of analysis tools, Social Network Analysis (SNA) has received much interest to model real-world phenomena in a variety of domains, e.g., research collaboration and have a better perception of social events. Research collaboration refers to the main procedure of integrating disorganized capabilities and knowledge into novel research techniques and ideas. The invaluable analytical indicator of research collaboration is the outcome of analyses of scientific articles developed as its achievements. This article investigates collaboration and co-authorship in the Process Mining field based on the already published dataset consisting of 1278 papers which are selected by their keywords or snowball technique. According to crucial results, the co-authorship network developed among researchers features a number of the properties of the scale-free networks. Additionally, using mathematics, it has been proven that the acquired network is small world network. Besides, most central authors are determined by integrating four centrality measures include closeness, degree, eigenvector, and betweenness via TOPSIS. This network has been compared and reviewed in the absence/presence of such actors. In accordance with the obtained affiliation of the high-ranking authors, TU/e university plays the most pivotal role in Process Mining promotion.