This paper studies the community structure of the bank correlation network in the financial system and analyzes the systemic risk of the community sub-networks. Based on the balance sheet data of U.S. commercial banks from 2008, we establish a bank correlation network for each state according to the banks’ investment portfolio ratio. First, we analyze the community structure of each bank’s correlation network and verify the effectiveness of the community division from the point of view of the importance of nodes. Then, combining the data of failed banks after the 2008 financial crisis, we find that for small communities, the financial systemic risk will appear to have obvious volatility, and it is quite likely to reach an extremely high level. With the increase in the number of nodes in the community, systemic risk will tend towards a stable and low level. Furthermore, if only communities with failed banks are considered, the regression analysis shows that systemic risk and the size of the community almost follow a power law distribution trend. These results reveal the importance of supervising the banking system at the level of community sub-networks, which has certain guiding significance for the stability of the financial system.

This paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projected network and a stock projected network, whose structural characteristics can intuitively reveal the overlapping portfolios among different shareholders, as well as shareholder allocation structures among different stocks. The degree of nodes in the shareholder projected network obeys the power law distribution, the network aggregation coefficient is large, while the degree of most nodes in the stock projected network is small and the network aggregation coefficient is low. Furthermore, the two projected networks’ community structures are analyzed, respectively. Most of the communities in the shareholder projected network and stock projected network are small-scaled, indicating that the majority of large shareholders hold different shares from each other, and the investment portfolios of large shareholders in different stocks are also significantly different. Finally, by comparing the stock projected sub-network obtained from the shareholder-stock bipartite sub-network in which the degree of shareholder nodes is 2 and the original stock projected network, the effectiveness of the statistically validated network model, and the community division method on the research of the shareholder-stock bipartite network are further verified. These results have important implications for understanding the investment behavior of large shareholders in the stock market and contribute to developing investment strategies and risk management practices.