Detecting emerging research fronts based on topological measures in citation networks of scientific publications
Detecting emerging research fronts based on topological measures in citation networks of scientific publications
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DOI：
10.1016/j.technovation.2008.03.009
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发表时间：
2008-11
期刊：
影响因子：
12.5
通讯作者：
N. Shibata;Y. Kajikawa;Y. Takeda;K. Matsushima
中科院分区：
文献类型：
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作者：
N. Shibata;Y. Kajikawa;Y. Takeda;K. Matsushima
在本文中，我们进行了比较研究，在两个研究领域，以开发一种方法来检测新兴的知识领域。选定的领域是对氮化镓（GaN）的研究和对复杂网络的研究，这是最近创新研究的例子。我们使用拓扑聚类方法将引文网络划分为簇，跟踪每个簇中论文的位置，并使用每个簇的特征术语可视化引文网络。通过对聚类结果的分析，结合聚类的平均年龄和亲子关系，可以帮助我们更好地发现幼儿的涌现现象。此外，拓扑措施，集群内的程度z和参与系数P，成功地确定是否有新兴的知识集群。知识领域的发展至少有两种类型。一种是GaN中的渐进式创新，另一种是复杂网络中的分支创新。在渐进式创新的领域，论文的位置变化为z大、P大。另一方面，在分支创新的领域，论文的位置变化为z大、P小，因为出现了新的集群，活跃的研究中心迅速转移。我们的研究结果表明，拓扑措施是有益的，在检测分支创新的科学出版物的引文网络。
In this paper, we performed a comparative study in two research domains in order to develop a method of detecting emerging knowledge domains. The selected domains are research on gallium nitride (GaN) and research on complex networks, which represent recent examples of innovative research. We divided citation networks into clusters using the topological clustering method, tracked the positions of papers in each cluster, and visualized citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average age and parent–children relationship of each cluster may be helpful in detecting emergence. In addition, topological measures, within-cluster degree z and participation coefficient P, succeeded in determining whether there are emerging knowledge clusters. There were at least two types of development of knowledge domains. One is incremental innovation as in GaN and the other is branching innovation as in complex networks. In the domains where incremental innovation occurs, papers changed their position to large z and large P. On the other hand, in the case of branching innovation, they moved to a position with large z and small P, because there is a new emerging cluster, and active research centers shift rapidly. Our results showed that topological measures are beneficial in detecting branching innovation in the citation network of scientific publications.