Link Prediction Based on Clustering Information in Scientific Coauthorship Networks

Link prediction in social networks has become a growing concern among researchers. In this paper, clustering index (CI) is calculated to predict potential links with characteristics of scientific cooperation network taken into consideration. Compared with traditional universal algorithms, algorithm for specific type of network can better reflect the features of the network and achieve better predicting results. Experiments show that CI performs better than traditional indices in scientific coauthorship networks.