Will This Paper Increase Your h-index?: Scientific Impact Prediction

Yuxiao Dong, Reid A. Johnson, N. Chawla
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引用次数: 91

Abstract

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."
这篇论文会提高你的h指数吗?科学影响预测
在评估学者、部门和机构的产出时,科学影响起着核心作用。广泛使用的科学影响衡量标准是引用次数,越来越多的文献关注于预测任何给定出版物获得的引用次数。然而,这种预测的有效性从根本上受到引用的幂律分布的限制,因此引用很少的出版物非常普遍,而引用很多的出版物相对较少。考虑到这一限制,在这项工作中,我们转而解决许多学术研究人员在撰写论文过程中提出的一个相关问题,即:“这篇论文会提高我的h指数吗?”我们使用来自一流在线学术服务ArnetMiner的真实学术数据集,其中包含超过170万作者,200万篇论文和800万篇引用关系,我们形式化了一个新的科学影响预测问题,以检查可以驱动论文增加主要作者h指数的几个因素。我们发现,研究者对发表主题的权威性和论文发表地点是第一作者h指数提高的关键因素,而主题知名度和共同作者h指数的相关性令人惊讶地小。通过利用相关因素,我们发现一篇论文是否会在五年内对作者的h指数做出贡献的潜在可预测性大于87.5%。作为进一步的实验,我们对这篇论文进行了自我预测,估计有76%的概率它将在五年内贡献当前h指数最高的合著者的h指数。我们的结论是,我们对科学影响量化的发现可以帮助研究人员扩大他们的影响力,更有效地利用他们“站在巨人的肩膀上”的地位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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