With the wide application of statistics, it is important to identify research trends and the development of statistics. In this paper, we analyze a citation network of the top 4 statistical journals from 2001 to 2018, applying the directed spectral clustering on the ratio-of-eigenvectors (D-SCORE) method to detect the community structure of citation network. We find that statistical researchers are becoming more and more collaborative. The number of influential papers which account for the majority of citations is small. High betweenness centrality and high closeness centrality papers are concentrated in Annals of Statistics (AoS). Furthermore, we detect 4 communities and 11 sub-communities such as “High-dimensional Model”, “Variable Selection”, and “Covariance Matrix Analysis”. Then, we compare the results of D-SCORE with three other methods and find that D-SCORE is more suitable for our citation network. Finally, we identify the dynamic nature of the communities. Our findings present trends and topological patterns of statistical papers, and the data set provides a fertile ground for future research on social networks.

中文翻译：

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通过D-SCORE对统计引用网络进行社区检测

随着统计学的广泛应用，重要的是确定研究趋势和统计学的发展。在本文中，我们分析了2001年至2018年排名前4位的统计期刊的引文网络，应用基于特征向量比（D-SCORE）方法的定向光谱聚类来检测引文网络的社区结构。我们发现统计研究人员变得越来越合作。占大多数引用量的有影响力的论文数量很少。高度中间性和高度紧密性论文集中在《统计年鉴》（AoS）中。此外，我们检测到4个社区和11个子社区，例如“高维模型”，“变量选择”和“协方差矩阵分析”。然后，我们将D-SCORE的结果与其他三种方法进行了比较，发现D-SCORE更适合我们的引文网络。最后，我们确定了社区的动态性质。我们的发现提出了统计论文的趋势和拓扑模式，并且该数据集为将来对社交网络的研究提供了沃土。