Abstract
Recent works aimed to understand how to identify “milestone” scientific papers of great significance from largescale
citation networks. To this end, previous results found that global ranking metrics that take into account the whole
network structure (such as Google’s PageRank) outperform local metrics such as the citation count. Here, we show that
by leveraging the recursive equation that defines the PageRank algorithm, we can propose a family of local versions
of PageRank with finite iterations. Our results reveal that these PageRank-based local metrics outperform the citation
count and other local metrics in identifying the seminal papers, and compared with global metrics, these local metrics
can reach similar performance in the identification of seminal papers with less time overhead and no requirement for
the whole network topology. Our findings could help to better understand the nature of groundbreaking research from
citation network analysis and find practical applications in large-scale data.
Wang, Jingjing