Quantifying mesoscopic structure dynamics in stock market using eigenvector centrality and Sectoral entropy
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Structural entropy refers to the level of heterogeneity of nodes in the network, with the premise that nodes that share functionality or attributes are more connected than others. The correlation-based network of sectoral communities in a market change with time. We demonstrate that a novel entropy measure – eigen-entropy, computed from the eigen-centralities (ranks) of different stocks in the correlation-network, is able to extract information about the market disorder.