Community detection for statistical citation network by D-SCORE

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.

Keywords

community detection, D-SCORE, citation network

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The authors’ research was supported by National Natural Science Foundation of China (NSFC, 11971504, 11631003, 12001557, 71771224), the Fundamental Research Funds for the Central University of Finance and Economics (QL18010), the Youth Talent Development Support Program (QYP1911), the Program for Innovation Research in Central University of Finance and Economics, and the disciplinary funding of Central University of Finance and Economics.

Received 25 February 2020

Accepted 6 September 2020

Published 9 February 2021