Link Prediction in Scientists Collaboration with Author Name and Affiliation

Link prediction task is to discover the indirect relationships from the network based on present information. We studied scientists/authors network and added affiliation information into the author's network in order to enhance the performance of link prediction. In this study, for ranking the collaborations/edges, we have used edge based scoring algorithms rather than node based scoring algorithms and used bipartite graph data structure for finding active authors. We have introduced a feature named Author's Diversity/Similarity score that describes the probability of an author to write with the same or different affiliation. The performance of our proposed algorithm has increased significantly comparing with the existing system.