一种从原始引文网络中发现科学特征的新方法
A new approach for detecting scientific specialties from raw cocitation networks
摘要：
我们使用了Blondel等人（2008）最近开发的一种技术，以便从作者引文网络中检测科学专业。该算法避免了相似性度量的使用，完全依赖于加权网络的拓扑结构，适用于较大的网络，因此与以往大多数求引用“簇”的方法相比具有明显的优势。最重要的是，它不需要主观解释的引文数据或社区found.Using两个例子，我们表明，由此产生的专业是最小的连贯的“组”的研究人员（在一个层次的集群大小），因此可以确定明确。此外，我们确认，这些社区确实代表了我们所知道的关于给定科学学科的结构，并且作为专业，它们可以通过几个关键字（从出版物标题）准确地表征。我们认为，这种强大而有效的算法是特别适合引用网络，并且所产生的结果可以是非常有用的研究人员研究的结构和科学的演变的各个方面。
英文摘要：
We use a technique recently developed by Blondel et al. (2008) in order to detect scientific specialties from author cocitation networks. This algorithm has distinct advantages over most of the previous methods used to obtain cocitation "clusters", since it avoids the use of similarity measures, relies entirely on the topology of the weighted network and can be applied to relatively large networks. Most importantly, it requires no subjective interpretation of the cocitation data or of the communities found. Using two examples, we show that the resulting specialties are the smallest coherent "group" of researchers (within a hierarchy of cluster sizes) and can thus be identified unambiguously. Furthermore, we confirm that these communities are indeed representative of what we know about the structure of a given scientific discipline and that, as specialties, they can be accurately characterized by a few keywords (from the publication titles). We argue that this robust and efficient algorithm is particularly well-suited to cocitation networks, and that the results generated can be of great use to researchers studying various facets of the structure and evolution of science.
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