Investigation of academic collaborations as social network and determination of dominating set

Social networks are dynamic and constantly growing. These are complex structures where inferences about social network are made by analyzing the interactions between actors from different perspectives. Social network analysis is to obtain meaningful information from these data collections using various methods. Social networks can be analyzed in many ways because the data that form the network; it consists of a wide range covering various fields of life such as education, health, social media etc. Dominating set analysis is one of the research areas that occupy a large place in the literatüre. The dominating set problem is the determination of the dominating cluster with the minimum number of nodes in a complex network. In a complex network,it is important to evaluate centrality measures that provide information about nodes position in the network in which they are located for the identification of dominating cluster. As technology evolves, internet usage increases rapidly. Considering this situation, useful results can be obtained in many research areas with analysis of social networks. By modeling complex data in networks such as business networks, academic collaboration networks, user interaction networks, product-customer interaction networks, disease networks, results can be produced to solve the problems encountered in real life. In this study, an academic collaboration network has been established from the data consisting of universities in Turkey and data analysis was performed on these network. While creating the academic collaboration network, the data categorized based on research areas and geografic establishment, and modeled as a social network consisting of publications made by the authors. After the modeling phase, the data was visualized. During the analysis phase, local and non-local centrality measueres were calculated by considering the general positions of each university in the academic collaboration network. Benefiting the these measures, the dominant universities were identified in the network.