Finding potential research collaborators in four degrees of separation
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Time aware index for link prediction in social networks
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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Although there have been many recent studies of link prediction in co-authorship networks, few have tried to utilize the Semantic information hidden in abstracts of the research documents. We propose to build a link predictor in a co-authorship network where nodes represent researchers and links represent co-authorship. In this method, we use the structure of the constructed graph, and propose to add a semantic approach using abstract information, research titles and the event information to improve the accuracy of the predictor. Secondly, we make use of the fact that researchers tend to work in close knit communities. The knowledge of a pair of researchers lying in the same dense community can be used to improve the accuracy of our predictor further. Finally, we test out hypothesis on the DBLP database in a reasonable time by under-sampling and balancing the data set using decision trees and the SMOTE technique.