Community detection and co-author recommendation in co-author networks

With the increasing complexity of scientific research and the expanding scale of projects, scientific research cooperation is an important trend in large-scale research. The analysis of co-authorship networks is a big data problem due to the expanding scale of the literature. Without sufficient data mining, research cooperation will be limited to a similar group, namely, a “small group”, in the co-author networks. This “small group” limits the research results and openness. However, the researchers are not aware of the existence of other researchers due to insufficient big data support. Considering the importance of discovering communities and recommending potential collaborations from a large body of literature, we propose an enhanced clustering algorithm for detecting communities. It includes the selection of an initial central node and the redefinition of the distance and iteration of the central node. We also propose a method that is based on the hilltop algorithm, which is an algorithm that is used in search engines, for recommending co-authors via link analysis. The co-author candidate set is improved by screening and scoring. In screening, the expert set formation of the hilltop algorithm is added. The score is calculated from the durations and quantity of the collaborations. Via experiments, communities can be extracted, and co-authors can be recommended from the big data of the scientific research literature.