Mixed-score community detection method for weighted stock networks

The stock network is a financial knowledge graph where nodes represent stocks and edges capture their relationships, forming a weighted network. Community detection in such networks is essential for sector division, enabling investors to identify market trends and optimize investment strategies. Traditional approaches typically rely on disjoint community detection, assigning each stock to a single sector, or conventional overlapping community detection, assigning stocks to multiple sectors without quantifying their degree of association with each. To address these limitations, this study introduces the Mixed-SCORE method for identifying overlapping communities in weighted stock networks, termed the Weighted Mixed-SCORE method. Unlike traditional methods, this approach distinguishes between pure nodes (exclusive to one community) and mixed nodes (associated with multiple sectors with varying weights). By leveraging network weights, the method offers a more nuanced understanding of stock relationships and sector structures. Using data from 469 S&P 500 stocks between 2018 and 2022, we demonstrate the effectiveness of the Weighted Mixed-SCORE method in uncovering overlapping community structures. Our analysis reveals the central role of pure nodes within their respective communities and examines how mixed nodes bridge multiple sectors, providing valuable insights for market analysis and investment decision-making. This study not only advances the application of community detection in financial networks but also offers a robust tool for sector analysis and portfolio optimization.