通过引用网络的传递约简揭示跨学科性
Interdisciplinarity Revealed by Transitive Reduction of Citation Networks
AI总结：
该研究提出通过引用网络传递约简后的引用损失量判断文献跨学科性的假设，结合人工模型与三类引用数据、聚类方法验证，在多数案例中得到支持，为跨学科文献识别提供了新思路。
摘要：
我们研究了传递约简对引用网络的影响。我们的假设是：在传递约简过程中损失引用量更少的文献可能具有跨学科性，而引用量损失较大则表明该文献主要在单一学科内被引用。我们通过一个引用网络人工模型，以及来自学术论文、法院判决和专利三个来源的引用数据来验证这一假设。在需要时，我们对基于文献耦合构建的网络应用基于模块度的聚类技术，按主题对文献进行分类。随后使用一种依赖聚类的度量指标，将节点划分为跨学科或学科内类型。我们的结果在四个案例中的三个里为假设提供了强有力的支持，在专利案例中支持力度稍弱但仍为正向。
英文摘要：
We investigate the impact of transitive reduction on citation networks. Our hypothesis is that documents which lose fewer citations under transitive reduction are likely to be interdisciplinary, while a large loss of citations suggests a document is primarily cited within a single discipline. We test this hypothesis by using an artificial model of a citation network and by using data on citations from three sources: academic papers, court decisions and patents. Where needed, we applied modularity-based clustering techniques on a network defined using bibliographic coupling to classify documents by topic. A cluster-dependent measure was then used to classify the nodes as interdisciplinary or intradisciplinary. Our results provide strong support for our hypothesis in three of the four cases, with somewhat weaker but still positive support in the case of patents.
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