Coauthor Prediction for Junior Researchers

  • Shuguang Han
  • Daqing He
  • Peter Brusilovsky
  • Zhen Yue
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7812)

Abstract

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach.

Keywords

Coauthor prediction link prediction social network expert search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shuguang Han
    • 1
  • Daqing He
    • 1
  • Peter Brusilovsky
    • 1
  • Zhen Yue
    • 1
  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUnited States

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