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Finding Experts by Link Prediction in Co-authorship Networks

, and . Proceedings of the Workshop on Finding Experts on the Web with Semantics (FEWS2007) at ISWC/ASWC2007, Busan, South Korea, (November 2007)

Abstract

Research collaborations are always encouraged, as they often yield good results. However, the researcher network contains massive amounts of experts in various disciplines and it is difficult for the individual researcher to decide which experts will match his own expertise best. As a result, collaboration outcomes are often uncertain and research teams are poorly organized. We propose a method for building link predictors in networks, where nodes can represent researchers and links - collaborations. In this case, predictors might offer good suggestions for future collaborations. We test our method on a researcher co-authorship network and obtain link predictors of encouraging accuracy. This leads us to believe our method could be useful in building and maintaining strong research teams. It could also help with choosing vocabulary for expert description, since link predictors contain implicit information about which structural attributes of the network are important with respect to the link prediction problem.

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BibTeX key:
Pavlov/2007/Finding
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