The influence of disruption on evaluating the scientific significance of papers

• Published in: Scientometrics 2022, Vol.127 (10), p.5931-5945
• Main Authors: Zhou, Yanbo, Xu, Xin-Li, Yang, Xu-Hua, Li, Qu
• Format: Article
• Language: English
• Subjects: Benchmarks; Computer Science; Decay; Decay rate; Disruption; Information Storage and Retrieval; Library Science; Performance evaluation; Scientific papers; Scientometrics
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-022-04505-4

With the exponential growth of the volume of scientific literatures, academic evaluation is becoming one of the important problems of scientometrics. Considering the influence of disruption of papers, in this paper, we propose a disruption based PageRank (DPRank) model to rank the scientific significance of scientific papers. The disruption value of a paper is calculated by the local network structure of the paper in the citation network. DPRank model treats each citation differently based on paper’s temporal information and disruption value. In DPRank model, the scores delivered from a paper’s upstream nodes decay exponentially with the time gap between the publication time and reference time, and the decay rate is proportional to the paper’s disruption value. We use the APS data set and some highly valued papers as benchmarks to evaluate the performance of the proposed DPRank model. The experimental results show that the DPRank model has a relatively better performance for both old and newly published papers, and can outperform other methods in identifying the benchmark papers.