Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation

IEEE Transactions on Knowledge and Data Engineering(2021)

引用 25|浏览31
暂无评分
摘要
As the rate at which scientific work is published continues to increase, so does the need to discern high-impact publications. In recent years, there have been several approaches that seek to rank publications based on their expected citation-based impact. Despite this level of attention, this research area has not been systematically studied. Past literature often fails to distinguish between short-term impact, the current popularity of an article, and long-term impact, the overall influence of an article. Moreover, the evaluation methodologies applied vary widely and are inconsistent. In this work, we aim to fill these gaps, studying impact-based ranking theoretically and experimentally. First, we provide explicit definitions for short-term and long-term impact, and introduce the associated ranking problems. Then, we identify and classify the most important ideas employed by state-of-the-art methods. After studying various evaluation methodologies of the literature, we propose a specific benchmark framework that can help us better differentiate effectiveness across impact aspects. Using this framework we investigate: (1) the practical difference between ranking by short- and long-term impact, and (2) the effectiveness and efficiency of ranking methods in different settings. To avoid reporting results that are discipline-dependent, we perform our experiments using four datasets from different scientific disciplines.
更多
查看译文
关键词
Bibliometrics,information retrieval,data mining
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要