Future Influence Ranking of Scientific Literature

TL;DR

This paper introduces MRFRank, a unified model that predicts the future influence of scientific papers and researchers by combining text features and time-aware graphs, outperforming existing methods.

Contribution

The paper presents a novel mutual reinforcement ranking framework that integrates text features and dynamic graphs to forecast future scientific impact.

Findings

MRFRank outperforms baselines on recommendation intensity

Time-aware weighted graphs improve importance distinction

Text features effectively characterize innovative papers and authors

Abstract

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}.