引文网络的时变影响测量

M. Rakoczy, A. Bouzeghoub, Alda Lopes Gançarski, K. Wegrzyn-Wolska
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引用次数: 0

摘要

在每一个科学学科中,研究人员都面临着两个共同的困境:在哪里找到最前沿的论文,在哪里发表自己的文章。我们建议通过观察社区之间的影响来回答这些问题,例如会议或期刊。有影响力的会议是那些论文被其他会议大量引用的会议,即它们是可见的,重要的和鼓舞人心的。为了找到这样有影响力的发表地点,我们引入了一个运行影响力模型,旨在发现社区之间的成对影响,并评估每个考虑社区的整体影响力。我们考虑了时间方面的因素,如论文被引用的强度和会议开始年份的差异。社区影响分析在计算机科学会议的真实数据上进行了测试。
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Time-Dependent Influence Measurement in Citation Networks
In every scientific discipline, researchers face two common dilemmas: where to find bleeding-edge papers and where to publish their own articles. We propose to answer these questions by looking at the influence between communities, e.g. conferences or journals. The influential conferences are those which papers are heavily cited by other conferences, i.e. they are visible, significant and inspiring. For the task of finding such influential places-to-publish, we introduce a Running Influence model that aims to discover pairwise influence between communities and evaluate the overall influence of each considered community. We have taken into consideration time aspects such as intensity of papers citations over time and difference of conferences starting years. The community influence analysis is tested on real-world data of Computer Science conferences.
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