The dynamic nature of citation networks makes the task of ranking scientific articles hard. Citation networks are continually evolving because articles obtain new citations every day. For ranking scientific articles, we can define the popularity or prestige of a paper based on the number of past citations at the user query time; however, we argue that what is most useful is the expected future references. We define a new measure, FutureRank, which is the expected future PageRank score based on citations that will be obtained in the future. In addition to making use of the citation network, FutureRank uses the authorship network and the publication time of the article in order to predict future citations. Our experiments compare FutureRank with existing approaches, and show that FutureRank is accurate and useful for finding and ranking publications.
Sayyadi and Getoor REF propose the FutureRank to predict the future influence of scholarly articles through making use of the citation network which considered the publication time of the article.
FutureRank: Ranking Scientific Articles by Predicting their Future PageRank
In Proc. of the 9th SIAM International Conference on Data Mining
Computer Science