ArticleScientific reports2019
Structural Entropy: Monitoring Correlation-Based Networks Over Time With Application To Financial Markets.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 54 citations in OpenAlex.
- Selected Topics in Time Series Forecasting: Statistical Models vs. Machine Learning.Entropy (Basel, Switzerland) · 2025Review
- Analysis of the Spatiotemporal Heterogeneity and Influencing Factors of Regional Economic Resilience in China.Entropy (Basel, Switzerland) · 2024Article
- Inferring Dealer Networks in the Foreign Exchange Market Using Conditional Transfer Entropy: Analysis of a Central Bank Announcement.Entropy (Basel, Switzerland) · 2024Article
- CCTFv2: Modeling Cyber Competitions.Entropy (Basel, Switzerland) · 2024Article
- Entropy of Financial Time Series Due to the Shock of War.Entropy (Basel, Switzerland) · 2023Article
- Sovereign Bond Yield Differentials across Europe: A Structural Entropy Perspective.Entropy (Basel, Switzerland) · 2023Article
- Shannon Entropy: An Econophysical Approach to Cryptocurrency Portfolios.Entropy (Basel, Switzerland) · 2022Article
- Mutual Information between Order Book Layers.Entropy (Basel, Switzerland) · 2022Article
- Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network.Entropy (Basel, Switzerland) · 2021Article
- Network geometry and market instability.Royal Society open science · 2021Article
- An improved algorithm for the maximal information coefficient and its application.Royal Society open science · 2021Article
- Sentiment correlation in financial news networks and associated market movements.Scientific reports · 2021Article
- Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The concept of "Structural Diversity" of a network refers to the level of dissimilarity between the various agents acting in the system, and it is typically interpreted as the number of connected components in the network. This key property of networks has been studied in multiple settings, including diffusion of ideas in social networks and functional diversity of regions in brain networks. Here, we propose a new measure, "Structural Entropy", as a revised interpretation to "Structural Diversity". The proposed measure relies on the finer-grained network communities (in contrast to the network's connected components), and takes into consideration both the number of communities and their sizes, generating a single representative value. We then propose an approach for monitoring the structure of correlation-based networks over time, which relies on the newly suggested measure. Finally, we illustrate the usefulness of the new approach, by applying it to the particular case of emergent organization of financial markets. This provides us a way to explore their underlying structural changes, revealing a remarkably high linear correlation between the new measure and the volatility of the assets' prices over time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.