## 1 Introduction  
Time series analysis and complex networks in the field of statistical physics are the main research areas for decades. In particular, non-stationary time series has been considered as a difficult field to work with and various methods have been developed in an effort to analyze these time series. R/S analysis, detrended fluctuation analysis (DFA) [1, 2], and detrended cross correlation analysis (DCCA) [3] have been known to detect long-range correlation of time series by applying them to non-stationary time series. In addition, the complex network enables a new perspective analysis by expressing a system as nodes and edges. These two analytical methods have been applied in a variety of fields, such as physics [4], economics [5], biology [6, 7], physiology [8], climate science [9], brain sciences [10], and so on. [PERSON] [11] proposed the new correlation coefficient, which is the DCCA correlation coefficient that quantifies non-stationary time series using the DFA and DCCA methods. [PERSON] and [PERSON] [12] compared the correlation coefficients of DCCA and Pearson methods. They used temperature and relative humidity data from stations in Beijing, Jinan, Shijazhuang, and Zhengzhou to show that the correlation coefficient of DCCA was more accurate than Pearson in the annual time series. [PERSON] and [PERSON] [13] calculated modularity to determine community structures in the network: modularity is a quantitative measurement of the network's optimal community structure. They calculated the betweenness centrality of an edge of the network to remove the edge with the highest centrality value and calculate the modularity again. Subsequently, the structure of the network with the highest modularity value represents the best separation of groups within the network. [PERSON] et al. [14] developed the [PERSON] and [PERSON]'s methods as multiple edges. In addition, [PERSON] et al. [15] analyzed the network community structure of a stock market using the _Girvan-Newman method_ for 180-index data registered in the SSE (Shanghai Stock Exchange). They used Pearson correlation coefficients to build the network by varing the thresholds. They found that the network with a threshold 0.7 was a proper structure than the others. By detecting a community structure of the network, they found that corporations in the same industry belong to the same community. [PERSON] et al. [16] built a network using the Pearson correlation coefficient for 348 stocks in the New York Stock Exchange. They created a network that reflected time evolution, and analyzed the network structure before, after, and during the Black Monday crisis. They found that, during the crisis, the modularity values were lower than other network structures. [PERSON] et al. [17] conducted a network analysis of 710 stocks of the Shanghai Stock Exchange (SSE). They calculated the global efficiency on a yearly basis for 2005-2011, it was confirmed that the global efficiency sharply decreased when the subprime crisis occurred. [PERSON] et al. [18] performed a network analysis of the pre-subprime crisis period and post-subprime crisis period using the stock market indices of 20 countries for the period 2001-2017. They used the DCCA method and created a weighted network by connecting stock markets with a DCCA factor of 0.66 or more. They applied the _Louvain method_ to detect the community structure. The network created three groups of (Europe), (America), and (Asia, Australia) during the pre-subprime crisis, and two groups of (Europe, America) and (Asia, Australia) during post-subprime crisis. There are other previous studies that performed network analysis on the stock market [19; 20]. Financial markets are treated as complex systems, so network analysis is an important tool for dealing with them [21]. The stock market can be seen as an area with both time series and network features. Changes in stock prices can be characterized by time series, and the network structure is defined by connecting the correlated stock markets [22].