A Bias-Free Time-Aware PageRank Algorithm for Paper Ranking in Dynamic Citation Networks
Date
2022-02-22
Authors
Abu Dayeh, Moath
Sartawi, Badie
Salah, Saeed
Journal Title
Journal ISSN
Volume Title
Publisher
Intelligent Information Management
Abstract
The process of ranking scientific publications in dynamic citation networks
plays a crucial rule in a variety of applications. Despite the availability of a
number of ranking algorithms, most of them use common popularity metrics
such as the citation count, h-index, and Impact Factor (IF). These adopted
metrics cause a problem of bias in favor of older publications that took enough
time to collect as many citations as possible. This paper focuses on solving the
problem of bias by proposing a new ranking algorithm based on the PageRank
(PR) algorithm; it is one of the main page ranking algorithms being widely used.
The developed algorithm considers a newly suggested metric called the Citation
Average rate of Change (CAC). Time information such as publication date and
the citation occurrence’s time are used along with citation data to calculate
the new metric. The proposed ranking algorithm was tested on a dataset of scien tific papers in the field of medical physics published in the Dimensions data base from years 2005 to 2017. The experimental results have shown that the
proposed ranking algorithm outperforms the PageRank algorithm in ranking
scientific publications where 26 papers instead of only 14 were ranked among
the top 100 papers of this dataset. In addition, there were no radical changes
or unreasonable jump in the ranking process, i.e., the correlation rate between
the results of the proposed ranking method and the original PageRank algo rithm was 92% based on the Spearman correlation coefficient
Description
Keywords
Citation
Dayeh, M. , Sartawi, B. and Salah, S. (2022) A Bias-Free Time-Aware PageRank Algorithm for Paper Ranking in Dynamic Citation Networks. Intelligent Information Management, 14, 53-70. doi: 10.4236/iim.2022.142004.