Predicting Collaboration Relationships on Social Network - Aslan, Serpil; Kaya, Buket (Institute of Electrical and Electronics Engineers Inc., 2019)
Scientific papers are mostly performed by collaboration between research groups. Researchers work in various subjects and in several research areas. Therefore, identifying a strong research group is a quite complex task. To cope with this complex task, we propose a network-based method that predicts the best appropriate collaborations for researchers in a co-authorship network. In this paper, we have constructed a weighted co-authorship network by using a real database of scientific papers. Almost all of the methods in this area use the unweighted network model. But, weighted networks are particularly important when representing the network-based properties. The constructed network consists of nodes and links which represent respectively authors and their collaborations. In these networks, if two researchers are the common authors in the same paper, they are considered connected. Typically, the link weights represent the number of papers that two authors have made collaboratively. We also use local LCP-based algorithms to calculate the betweenness and closeness of the authors in this paper. The experimental results demonstrate the success of the proposed method. © 2019 IEEE.
1st International Informatics and Software Engineering Conference, IISEC 2019 -- 6 November 2019 through 7 November 2019 -- Ankara -- 157111
Keywords: Co-authorship networks; Collaboration recommendations; LCP theory; Link Prediction