Threshold Cascade Dynamics in Coevolving Networks

Min, Byungjoon; Miguel, M. San

doi:10.3390/e25060929

Cited by 3 publications

(2 citation statements)

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“…The threshold model presents a discontinuous phase transition from a 'global cascade' phase to a 'no cascade' phase, which was analyzed in detail in [12]. This model has been extensively studied on various network topologies, such as regular lattices, small-world [13], random [15], clustered [16,17], modular [18], hypergraphs [19], homophilic [20] and coevolving [21] networks.…”

Section: Introductionmentioning

confidence: 99%

Ordering dynamics and aging in the symmetrical threshold model

Abella,

González-Avella,

San Miguel

et al. 2024

New J. Phys.

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The so-called Granovetter-Watts model was introduced to capture a situation in which the adoption of new ideas or technologies requires a certain redundancy in the social environment of each agent to take effect. This model has become a paradigm for complex contagion. Here we investigate a symmetric version of the model: agents may be in two states that can spread equally through the system via complex contagion. We find three possible phases: a mixed one (dynamically active disordered state), an ordered one, and a heterogeneous frozen phase. These phases exist for several configurations of the contact network. Then, we consider the effect of introducing aging as a non-Markovian mechanism in the model, where agents become increasingly resistant to change their state the longer they remain in it. We show that when aging is present, the mixed phase is replaced, for sparse networks, by a new phase with different dynamical properties. This new phase is characterized by an initial disordering stage followed by a slow ordering process towards a fully ordered absorbing state. In the ordered phase, aging modifies the dynamical properties. For random contact networks, we develop a theoretical description based on an Approximate Master Equation that describes with good accuracy the results of numerical simulations for the model with and without aging.

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Section: Introductionmentioning

confidence: 99%

Ordering dynamics and aging in the symmetrical threshold model

Abella,

González-Avella,

San Miguel

et al. 2024

New J. Phys.

Self Cite

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“…While these group interactions are common in reality, there is a gap addressing the coevolutionary dynamics driven by group interactions, pointing to the need for further examination in this area. Despite the initial research on the coevolutionary dynamics on networks based on group interactions for evolutionary games (Perc et al, 2013;Alvarez-Rodriguez et al, 2021), spin models (Mandrá et al, 2009), contagion dynamics (Lambiotte and González-Avella, 2011;Min and San Miguel, 2023), and voter models (Min and Miguel, 2017), there remain several issues for more extensive and thorough studies to grasp its full implications. An interesting example of coevolutionary dynamics with group interactions would be a coevolving nonlinear voter model (CNVM) (Min and Miguel, 2017;Kureh and Porter, 2020).…”

Section: Introductionmentioning

confidence: 99%

Coevolutionary dynamics of group interactions: coevolving nonlinear voter models

Min

2023

Front. Complex Syst.

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We survey the coevolutionary dynamics of network topology and group interactions in opinion formation, grounded on a coevolving nonlinear voter model. The coevolving nonlinear voter model incorporates two mechanisms: group interactions implemented through nonlinearity in the voter model and network plasticity demonstrated as the rewiring of links to remove connections between nodes in different opinions. We show that the role of group interactions, implemented by the nonlinearity can significantly impact both the dynamical outcomes of nodes’ state and the network topology. Additionally, we review several variants of the coevolving nonlinear voter model considering different rewiring mechanisms, noise of flipping nodes’ state, and multilayer structures. We portray the various aspects of the coevolving nonlinear voter model as an example of network coevolution driven by group interactions, and finally, present the implications and potential directions for future research.

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Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks

Wang,

Li,

Chen

2024

Entropy

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In social network analysis, entropy quantifies the uncertainty or diversity of opinions, reflecting the complexity of opinion dynamics. To enhance the understanding of how opinions evolve, this study introduces a novel approach to modeling opinion dynamics in social networks by incorporating three-stage cascade information attenuation. Traditional models have often neglected the influence of second- and third-order neighbors and the attenuation of information as it propagates through a network. To correct this oversight, we redefine the interaction weights between individuals, taking into account the distance of opining, bounded confidence, and information attenuation. We propose two models of opinion dynamics using a three-stage cascade mechanism for information transmission, designed for environments with either a single or two subgroups of opinion leaders. These models capture the shifts in opinion distribution and entropy as information propagates and attenuates through the network. Through simulation experiments, we examine the ingredients influencing opinion dynamics. The results demonstrate that an increased presence of opinion leaders, coupled with a higher level of trust from their followers, significantly amplifies their influence. Furthermore, comparative experiments highlight the advantages of our proposed models, including rapid convergence, effective leadership influence, and robustness across different network structures.

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Threshold Cascade Dynamics in Coevolving Networks

Cited by 3 publications

References 65 publications

Ordering dynamics and aging in the symmetrical threshold model

Ordering dynamics and aging in the symmetrical threshold model

Coevolutionary dynamics of group interactions: coevolving nonlinear voter models

Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social Networks

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