Personalization of the collaborator recommendation system in multi-layer scientific social networks: A case study of ResearchGate

The development of knowledge sharing platforms, like scientific social networks encourages researchers to establish international collaboration in scientific projects. After reviewing the previous methods for collaborator detection in social networks, gaps in the earlier models are investigated, and the present study aims at filling the gaps by introducing a new scientific collaborator recommendation system. Accordingly, in the present paper, an integrated model is presented based on multilayer networks that can personalize the proposing scientific collaborators. Our proposed model involves various types of collaboration features based on researchers' needs. In our method a scientific social network is modelled as a multi-relational network (MRN), in which collaborators are determined by a community detection algorithm. It provides us with an approach to integrate the personalized features into the collaborator detection model that is due to the essence of semi-supervisory learning of our community detection algorithm. This MRN helps us prevent information loss in the network. We considered two techniques for examining the models. The first one was General Collaborator Recommendation, and the second one was a Collaborator with Personalization Ability. The proposed method is applied to the data of the ResearchGate (RG) social network and is evaluated by criteria, such as F index and NMI.