FutureRank: Ranking scientific articles by predicting their future PageRank

The dynamic nature of citation networks makes the taskof 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.