Clustering and community detection in networks are of broad interest and have been the subject of extensive research that spans several fields. We are interested in the relatively narrow question of detecting communities of scientific publications that are linked by citations. These publication communities can be used to identify scientists with shared interests who form communities of researchers. Building on the well-known k-core algorithm, we have developed a modular pipeline to find publication communities. We compare our approach to communities discovered by the widely used Leiden algorithm for community finding. Using a quantitative and qualitative approach, we evaluate community finding results on a citation network consisting of over 14 million publications relevant to the field of extracellular vesicles.
翻译:网络的集群和社区探测引起了广泛的兴趣,是多个领域广泛研究的主题。我们感兴趣的是探测科学出版物社区这一相对狭窄的问题,这些出版物社区通过引文联系在一起。这些出版物社区可以用来识别具有共同利益的科学家,他们组成研究人员社区。根据众所周知的k-核心算法,我们开发了一个模块化管道以寻找出版社区。我们比较了我们与广泛使用的Leiden算法所发现的社区在社区发现的方法。我们采用定量和定性方法,评估社区在由1 400多万份与细胞外卵巢领域有关的出版物组成的引用网络上发现的结果。