title: Ranking scientific publications: the effect of nonlinearity.
abstract: Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing 100875, PR China.
authors.forename: Liyang
authors.initials: L
authors.lastname: Yao
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing 100875, PR China.
authors.forename: Tian
authors.initials: T
authors.lastname: Wei
authors.affiliation: 1] School of Systems Science, Beijing Normal University, Beijing 100875, PR China [2] Department of Physics, University of Fribourg, Fribourg CH1700, Switzerland.
authors.forename: An
authors.initials: A
authors.lastname: Zeng
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing 100875, PR China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Fan
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing 100875, PR China.
authors.forename: Zengru
authors.initials: Z
authors.lastname: Di