  Researchers or students entering a emerging research area are particularly
interested in what newly published papers will be most cited and which young
researchers will become influential in the future, so that they can catch the
most recent advances and find valuable research directions. However, predicting
the future importance of scientific articles and authors is challenging due to
the dynamic nature of literature networks and evolving research topics.
Different from most previous studies aiming to rank the current importance of
literatures and authors, we focus on \emph{ranking the future popularity of new
publications and young researchers} by proposing a unified ranking model to
combine various available information. Specifically, we first propose to
extract two kinds of text features, words and words co-occurrence to
characterize innovative papers and authors. Then, instead of using static and
un-weighted graphs, we construct time-aware weighted graphs to distinguish the
various importance of links established at different time. Finally, by
leveraging both the constructed text features and graphs, we propose a mutual
reinforcement ranking framework called \emph{MRFRank} to rank the future
importance of papers and authors simultaneously. Experimental results on the
ArnetMiner dataset show that the proposed approach significantly outperforms
the baselines on the metric \emph{recommendation intensity}.
