Link Prediction in Co-authorship Network
Research Area: Digital Libraries Year: 2014
Type of Publication: Article Keywords: Link prediction, supervised learning, co-authorship network
Authors:
  • Minh Le Nhat
 
   
Note:
Honours Year Project Report
Abstract:
Link prediction is one highlighted aspect of network analysis. Many popular applications include suggesting friends on Facebook and LinkedIn are using link prediction to feature its services. As a researcher, we are particularly interested in link prediction for scholar social network. In the research area, collaborations among authors often form a network of connections which defines the co-authorship network. It is proved that collaborations between authors/researchers often yield more fruitful results than individual researchers‟ performance. It comes to a sense that individual researcher might find himself want to collaborate with potential researchers. Predicting the evolution of co-authorship network thus plays a significant role in (1) analyzing the trend of the structure of scientific collaborations; (2) detecting potential research communities as well as their evolutions; (3) assessing the future influence of scientists; and (4) recommending companions, assistants, or colleagues for individual researchers. Realized the importance of link prediction, we are motivated to enhance its performance especially in co-authorship network. In this work, we propose new features that help to predict the collaborations among scholars, and analyze how these features work.
Digital version