Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking’s temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias.<\/p>\n","protected":false},"excerpt":{"rendered":"
Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. 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