论文标题

沿科学文章的观看曲线的突然变化的分类

Classification of abrupt changes along viewing profiles of scientific articles

论文作者

Brito, Ana C. M., Silva, Filipi N., de Arruda, Henrique F., Comin, Cesar H., Amancio, Diego R., Costa, Luciano da F.

论文摘要

随着电子发行的扩大,启动了科学文章传播的新动态。如今,甚至在出版之前,许多作品都以预印本的形式被广泛传播。另一个重要的新元素涉及已发表文章的观点。得益于某些期刊(例如PLOS One)提供了各自的数据,因此有可能在第一次引用出现之前,经常在出现第一次引用之前就如何观察科学作品。这提供了当前工作的主题。更具体地说,我们的研究是出于初步观察的激励,即随着时间的推移,视图曲线倾向于呈现分段线性的性质。然后划定了一种方法,以确定视图曲线中的主要段,该段允许得出几个相关的测量。特别是,我们专注于每个后续段的倾向和长度。基本统计数据表明,倾斜度可能会在随后的细分市场上有很大差异,而段的长度则更加稳定。考虑成对相关性的互补联合统计分析提供了有关视图属性的进一步信息。为了更好地理解视图曲线,我们进行了各自的多元统计分析,包括主成分分析和分层聚类。结果表明,一部分多边形视图被组织成簇或组。这些组的特征是原型,表明随后细分市场的相对增加或减少。然后开发了四个不同的模型来表示观察到的段。发现在段的性质之间结合了关节依赖性的模型在被考虑的替代方案中提供了最准确的结果。

With the expansion of electronic publishing, a new dynamics of scientific articles dissemination was initiated. Nowadays, many works are widely disseminated even before publication, in the form of preprints. Another important new element concerns the views of published articles. Thanks to the availability of respective data by some journals, such as PLoS ONE, it became possible to develop investigations on how scientific works are viewed along time, often before the first citations appear. This provides the main theme of the present work. More specifically, our research was motivated by preliminary observations that the view profiles along time tend to present a piecewise linear nature. A methodology was then delineated in order to identify the main segments in the view profiles, which allowed several related measurements to be derived. In particular, we focused on the inclination and length of each subsequent segment. Basic statistics indicated that the inclination can vary substantially along subsequent segments, while the segment lengths resulted more stable. Complementary joint statistics analysis, considering pairwise correlations, provided further information about the properties of the views. In order to better understand the view profiles, we performed respective multivariate statistical analysis, including principal component analysis and hierarchical clustering. The results suggest that a portion of the polygonal views are organized into clusters or groups. These groups were characterized in terms of prototypes indicating the relative increase or decrease along subsequent segments. Four respective distinct models were then developed for representing the observed segments. It was found that models incorporating joint dependencies between the properties of the segments provided the most accurate results among the considered alternatives.

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