In this mini-review, we critically examine the recent work done on correlation-based networks in financial systems. The structure of empirical correlation matrices constructed from the financial market data changes as the individual stock prices fluctuate with time, showing interesting evolutionary patterns, especially during critical events such as market crashes, bubbles, etc. We show that the study of correlation-based networks and their evolution with time is useful for extracting important information of the underlying market dynamics. Also, we present our perspective on the use of recently-developed entropy measures, such as structural entropy and eigen-entropy, for continuous monitoring of correlation-based networks.

在此小型审查中，我们将严格审查金融系统中基于相关网络的最新工作。由金融市场数据构建的经验相关矩阵的结构随着单个股票价格随时间的波动而变化，显示出有趣的演化模式，尤其是在诸如市场崩盘，泡沫等重大事件期间。基于相关的网络并且它们随时间的演变对于提取潜在市场动态的重要信息很有用。另外，我们对使用最近开发的熵测度（例如，结构熵 和 本征熵，用于持续监控基于相关性的网络。