The jamming transition is a k-core percolation transition

Morone, Flaviano; Burleson-Lesser, Kate; Vinutha, H. A.; Sastry, Srikanth; Makse, Hernán A.

doi:10.1016/j.physa.2018.10.035

Cited by 21 publications

(17 citation statements)

References 19 publications

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“…Further, weights of interactions and nonlinear threshold rules have been introduced to describe more complex dynamics [22,45,91]. Recently, the k-core was also applied as a precursor of the jamming transition in granular materials [92].…”

Section: K-core Percolation and Threshold Modelsmentioning

confidence: 99%

Influencer identification in dynamical complex systems

Pei

Wang

Morone

et al. 2019

Journal of Complex Networks

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The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e., the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g., independent cascading models) or discontinuous phase transitions (e.g., threshold models). We conclude the review with a summary and an outlook.

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Section: K-core Percolation and Threshold Modelsmentioning

confidence: 99%

Influencer identification in dynamical complex systems

Pei

Wang

Morone

et al. 2019

Journal of Complex Networks

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“…The concept of k-core was introduced in social sciences [14] to define network cohesion and was then applied in many other contexts [15], including the robustness of random networks [16], the structure of the internet [17,18], viral spreading in social networks [19], the large-scale structure of brain networks [20], and the jamming transition [21]. For a network of interacting species, the k-core is the portion of the network that remains after iteratively removing from the network all species linked to fewer than k other species (see Figs.…”

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confidence: 99%

The k-core as a predictor of structural collapse in mutualistic ecosystems

2018

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Collapses of dynamical systems into irrecoverable states are observed in ecosystems, human societies, financial systems and network infrastructures. Despite their widespread occurrence and impact, these events remain largely unpredictable. In searching for the causes for collapse and instability, theoretical investigations have been so far unable to determine quantitatively the influence of the structural features of the network formed by the interacting species. Here, we derive the condition for the stability of a mutualistic ecosystem as a constraint on the strength of the dynamical interactions between species and a topological invariant of the network: the k-core. Our solution predicts that when species located at the maximum k-core of the network go extinct as a consequence of sufficiently weak interaction strengths the system will reach the tipping point of its collapse. As a key variable involved in collapse phenomena, monitoring the k-core of the network may prove a powerful method to anticipate catastrophic events in the vast context that stretches from ecological and biological networks to finance. I. INTRODUCTIONA complex dynamical system collapses when a small perturbation in the parameters characterizing the species interactions causes a large-scale extinction of the species in the system [1][2][3][4][5][6][7][8][9][10][11][12]. The tipping point at which the system suddenly shifts to the irrecoverable state is, for practical purposes, the most important quantity one wishes to know [5,6,13]. It is a function of the dynamical and structural parameters of the system determined by the fixed point solution of the nonlinear equations describing the system's dynamics [1]. However, the tipping point is hard to determine, due to the difficulties encountered in solving the nonlinear dynamical equations to quantify the dependence of the fixed point solution on the system parameters and, in particular, on the features of the underlying network of interacting species in the system [1,3,6,8]. Indeed, no exact analytical result exists, so far, that relates the network properties to the fixed points of the dynamical system. Here, we first study numerically the fixed point equations of a dynamical system of mu-

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“…It was found that the average contact number, namely the coordination number, is a key parameter in particle systems 66 . The coordination number determines the particle contact network and plays an important role in clogging transition 67,68 . Thus, in our study, we employ the coordination number to distinguish particles at different states.…”

Section: Resultsmentioning

confidence: 99%

Mechanism for clogging of microchannels by small particles with liquid cohesion

Shao

Ruan

2021

AIChE Journal

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We numerically investigate the effect of liquid cohesion on the clogging of microchannels induced by small wet particles. The computer simulation is performed by the discrete element method (DEM) with cohesive contact models in presence of pendular liquid bridges, which is embedded into the computational fluid dynamics (CFD). We find that liquid cohesion significantly promotes particle deposition and agglomerate growth. A clogging phase diagram, in the form of Weber number and Stokes number, is constructed to quantify the clogging‐nonclogging transition. The competition between particle–particle and particle–fluid interactions is quantitatively discussed in terms of particle velocity and slip velocity. Strong cohesion can address a greater slip velocity or drag between particles and fluid, which depresses the resuspension of deposited particles and results in clogging. Finally, we compare our results with clogging induced by van der Waals adhesion of small dry particles and find that the competence of liquid cohesion is more prominent.

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The jamming transition is a k-core percolation transition

Cited by 21 publications

References 19 publications

Influencer identification in dynamical complex systems

Influencer identification in dynamical complex systems

The k-core as a predictor of structural collapse in mutualistic ecosystems

Mechanism for clogging of microchannels by small particles with liquid cohesion

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