Ranking Papers by their Short-Term Scientific Impact

• Published in: 2021 IEEE 37th International Conference on Data Engineering (ICDE) pp. 1997 - 2002
• Main Authors: Kanellos, Ilias, Vergoulis, Thanasis, Sacharidis, Dimitris, Dalamagas, Theodore, Vassiliou, Yannis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2021
• Subjects: citation networks; Conferences; Correlation; Data engineering; Data mining; paper ranking
• ISSN: 2375-026X
• DOI: 10.1109/ICDE51399.2021.00190

The constantly increasing rate at which scientific papers are published makes it difficult for researchers to identify papers that currently impact the research field of their interest. In this work, we present a method that ranks papers based on their estimated short-term impact, as measured by the number of citations received in the near future. Our method models a researcher exploring the paper citation network, and introduces an attention-based mechanism, akin to a time-restricted version of preferential attachment, that explicitly captures the researcher's preference to read papers which received a lot of attention recently. A detailed experimental evaluation on real citation datasets across disciplines, shows that our approach is more effective than previous work.