Effects of time-delays in the dynamics of social contagions

Wang, Wei; Stanley, H. Eugene; Braunstein, Lidia A.

doi:10.1088/1367-2630/aa9fc8

Cited by 24 publications

(15 citation statements)

References 48 publications

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“…In order to apply them to the context of financial contagion, we associate: Wang et al (2018) propose an updated version of the base continuous time model (Eq. 11) in which the same three states S, I and R are present.…”

Section: Models Of Population Dynamicsmentioning

confidence: 99%

“…Otherwise, the situations are very similar. The same three distinct states are recognised at each time step, although in Wang et al (2018) the infected state is called affected and is designated the A-state. A bank in the S-state has not yet been affected by a systemic shock because the stimulus received from its neighbours is below its adoption threshold.…”

Section: Models Of Population Dynamicsmentioning

confidence: 99%

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Systemic shock propagation in a complex system

Mitic

2019

Soft Comput

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We study the effects of delivering a shock to a complex system comprising components ('agents') that interact in a pairwise fashion, independent of other parts of the system and with no central control. There are three aspects to the contribution of this paper. First, shock propagation in a network is developed purely from fundamental principles of complex systems. Second, systemic risk is shown to arise naturally in such a complex system. If a shock is delivered either to one agent or to many agents simultaneously, that shock may be transmitted further, thereby resulting in systemic risk. Third, the monetary loss to the entire system as a result of systemic shock is quantified. Simulations are used to study two particular characteristics of the interactions. The first is the resistance or susceptibility of individual agents to a shock. The second is the time it takes for the shock to affect the entire system. The results show that if a shock is applied to all agents in a network, the systemic effect of that shock is transmitted very quickly. Applying a shock to very few agents results only in an idiosyncratic effect. If an agent can transmit the shock further, a systemic effect will result. The recovery period for agents affected by a systemic shock can be orders of magnitude greater than the time taken for the shock to take effect. The overall effect of the shock on the system is quantified by formulating a 'contagion index', which measures the ratio of the total capital lost due to the systemic effect to the total capital before the shock was delivered. The result (approximately 7%) is consistent with other studies, but is more widely applicable because it is not based on one empirical data set.

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Section: Models Of Population Dynamicsmentioning

confidence: 99%

Section: Models Of Population Dynamicsmentioning

confidence: 99%

Systemic shock propagation in a complex system

Mitic

2019

Soft Comput

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“…Time delays are pervasive real-world networked dynamics and can radically alter the evolution of dynamic processes in networks [203,264]. Greenhalgh et al introduced time delays into awareness-epidemic coevolution spreading dynamics [265].…”

Section: Coevolution Of Awareness and Epidemic On Single Networkmentioning

confidence: 99%

Coevolution spreading in complex networks

et al. 2019

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a b s t r a c tThe propagations of diseases, behaviors and information in real systems are rarely independent of each other, but they are coevolving with strong interactions. To uncover the dynamical mechanisms, the evolving spatiotemporal patterns and critical phenomena of networked coevolution spreading are extremely important, which provide theoretical foundations for us to control epidemic spreading, predict collective behaviors in social systems, and so on. The coevolution spreading dynamics in complex networks has thus attracted much attention in many disciplines. In this review, we introduce recent progress in the study of coevolution spreading dynamics, emphasizing the contributions from the perspectives of statistical mechanics and network science. The theoretical methods, critical phenomena, phase transitions, interacting mechanisms, and effects of network topology for four representative types of coevolution spreading mechanisms, including the coevolution of biological contagions, social contagions, epidemic-awareness, and epidemic-resources, are presented in detail, and the challenges in this field as well as open issues for future studies are also discussed.

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“…Information spreading on complex networks is an important subject in studying the spreading dynamics on complex networks, which aims at investigating the mechanism and laws of information diffusion [1], innovation adoption [2], healthy behavior [3]- [5] and financial behavior [6]. Researchers have explored many potential factors that may affect information spreading on complex networks, including node's attributes [7]- [10], edge's attributes [11], [12], community structure [13]- [16], and coreness [17].…”

Section: Introductionmentioning

confidence: 99%

Information Spreading on Two-Layered Multiplex Networks With Limited Contact

Yang

et al. 2020

IEEE Access

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Information spreading on multiplex complex networks has been widely concerned. In reality, the constraints of inelastic resources, such as energy, time, money, and an individual's restrict contact capacities, can affect the information spreading process. In this paper, we propose an information spreading model with limited contact capacity on the two-layered multiplex network. In order to quantitatively study the information spreading, we adopt an edge-based compartmental theory. We find that increasing the contact capacity facilitates information spreading. If the multiplex networks with strong heterogeneous degree distributions, the information outbreak size grows continuously with the transmission probability. However, when the information spreads on multiplex networks with homogeneous or heterogeneous degree distributions, there exists a crossover phenomenon, in which the information outbreak size versus the transmission probability can grow continuously or discontinuously. Our theory agrees well with the numerical simulations.

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Effects of time-delays in the dynamics of social contagions

Cited by 24 publications

References 48 publications

Systemic shock propagation in a complex system

Systemic shock propagation in a complex system

Coevolution spreading in complex networks

Information Spreading on Two-Layered Multiplex Networks With Limited Contact

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