The local structure of citation networks uncovers expert-selected milestone papers

Recent works aimed to understand how identify “milestone” scientific papers of great significance from large-scale citation networks. To this end, previous results found that global ranking metrics take into account the whole network structure (such as Google’s PageRank) outperform local such count. Here, we show by leveraging recursive equation defines PageRank algorithm, can propose a family impact metrics. Our reveal obtained PageRank-based count and other in identifying seminal papers. Compared with metrics, these reach similar performance identification shorter computational time, without requiring topology. findings could help better nature groundbreaking research analysis find practical applications data.