Dynamic Phase and Group Detection in Pedestrian Crowd Data Using Multiplex Visibility Graphs

Stephen, Colin

doi:10.1016/j.procs.2015.07.318

“…If you choose an oversized parameter ∆t, you might miss the actual crisis that distorts the indicator. Multiplex networks are actively used to simulate complex networks of different nature: from financial (banks [2], stock market [19], guarantee market [14]) to social [30]. Particular attention should be paid to the work [19], in which the above multiplex measures are analyzed for the subject of correlations with known stock markets crises.…”

Section: Experimental Results and Their Discussionmentioning

confidence: 99%

Complex networks theory and precursors of financial crashes

Soloviev¹,

Solovieva²,

Tuliakova³

et al. 2020

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Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex network of key cryptocurrencies. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock and crypto market, and their specific behaviour in the pre-crisis period is used as indicators- precursors of critical phenomena.

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“…Multiplex networks are actively used to simulate complex networks of different nature: from financial (stock market [336,337,339,340], banks [341], guarantee market [342]) to social [343]. Particular attention should be paid to the work [339], in which the above multiplex measures are analyzed for the subject of correlations with known stock market crises.…”

Section: Single and Multiplex Networkmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: an overview

Bielinskyi

¹

,

Serdyuk

²

,

Семеріков

³

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

show abstract

“…Multiplex networks are actively used to simulate complex networks of different nature: from financial (stock market [340,341,338,337], banks [342], guarantee market [343]) to social [344]. Particular attention should be paid to the work [340], in which the above multiplex measures are analyzed for the subject of correlations with known stock market crises.…”

Section: Single and Multiplex Networkmentioning

confidence: 99%

Econophysics of cryptocurrency crashes: a systematic review

Bielinskyi¹,

Serdyuk²,

et al. 2021

View full text Add to dashboard Cite

Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

show abstract

Dynamic Phase and Group Detection in Pedestrian Crowd Data Using Multiplex Visibility Graphs

Cited by 7 publications

References 11 publications

Complex networks theory and precursors of financial crashes

Complex networks theory and precursors of financial crashes

Econophysics of cryptocurrency crashes: an overview

Econophysics of cryptocurrency crashes: a systematic review

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