Using relaxational dynamics to reduce network congestion

Piontti, A.L. Pastore y; Rocca, C. E. La; Toroczkai, Zoltán; Braunstein, Lidia A.; Macri, P. A.

doi:10.1088/1367-2630/10/9/093007

Cited by 8 publications

(17 citation statements)

References 27 publications

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“…To this end we apply an algorithm that preserves the clustering and the degree distribution P (k), but allows us to change the degree correlations. Then, through this algorithm we can isolate the effects of the degree correlations from clustering on the pressure congestion and compare the results with the uncorrelated case [4]. In particular we argue that real world networks of communications evolve to a disassortative form in order to enhance the transport trough them.…”

Section: Introductionmentioning

confidence: 99%

“…In many cases of interest [1], this distribution is often scale free, which is characterized by a power-law degree distribution P (k) ∼ k −λ (k k min ), where k is the number of connections that a node can have, λ is the degree exponent and k min is the lowest degree allowed. The degree distribution has an important impact on the behavior of some dynamical processes taking place on the network, specially on the congestion problem [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…It is known [4] that for the uncorrelated case the pressure congestion increases with λ when the process has a relaxational component. In [4] it was shown that by introducing a surface relaxation mechanism, congestion in SF networks can be reduced, but the same mechanism has no effect on congestion in the case of Erdös Renyi random graphs [20].…”

Section: Introductionmentioning

confidence: 99%

“…In [4] it was shown that by introducing a surface relaxation mechanism, congestion in SF networks can be reduced, but the same mechanism has no effect on congestion in the case of Erdös Renyi random graphs [20]. In order to study the effects of the degree correlations of the underlying network on the transport, we measure the congestion pressure J .…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [4] the authors studied a dynamic process of gradient-induced flows produced by the local gradients of a non-degenerate scalar field…”

Section: Introductionmentioning

confidence: 99%

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Jamming in complex networks with degree correlation

2010

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We study the effects of the degree-degree correlations on the pressure congestion J when we apply a dynamical process on scale free complex networks using the gradient network approach. We find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally we apply our model to real world networks, and the results agree with our theoretical model

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In Ref. [4] the authors studied a dynamic process of gradient-induced flows produced by the local gradients of a non-degenerate scalar field…”

Section: Introductionmentioning

confidence: 99%

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Jamming in complex networks with degree correlation

2010

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Fluctuations of a surface relaxation model in interacting scale free networks

Torres

Rocca

Braunstein

2016

Physica A: Statistical Mechanics and its Applications

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Isolated complex networks have been studied deeply in the last decades due to the fact that many real systems can be modeled using these types of structures. However, it is well known that the behavior of a system not only depends on itself, but usually also depends on the dynamics of other structures. For this reason, interacting complex networks and the processes developed on them have been the focus of study of many researches in the last years. One of the most studied subjects in this type of structures is the Synchronization problem, which is important in a wide variety of processes in real systems. In this manuscript we study the synchronization of two interacting scale-free networks, in which each node has ke dependency links with different nodes in the other network. We map the synchronization problem with an interface growth, by studying the fluctuations in the steady state of a scalar field defined in both networks.We find that as ke slightly increases from ke = 0, there is a really significant decreasing in the fluctuations of the system. However, this considerable improvement takes place mainly for small values of ke, when the interaction between networks becomes stronger there is only a slight change in the fluctuations. We characterize how the dispersion of the scalar field depends on the internal degree, and we show that a combination between the decreasing of this dispersion and the integer nature of our growth model are the responsible for the behavior of the fluctuations with ke.

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Spatial correlation analysis of cascading failures: Congestions and Blackouts

Jiang

Kang

et al. 2014

Sci Rep

119

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Cascading failures have become major threats to network robustness due to their potential catastrophic consequences, where local perturbations can induce global propagation of failures. Unlike failures spreading via direct contacts due to structural interdependencies, overload failures usually propagate through collective interactions among system components. Despite the critical need in developing protection or mitigation strategies in networks such as power grids and transportation, the propagation behavior of cascading failures is essentially unknown. Here we find by analyzing our collected data that jams in city traffic and faults in power grid are spatially long-range correlated with correlations decaying slowly with distance. Moreover, we find in the daily traffic, that the correlation length increases dramatically and reaches maximum, when morning or evening rush hour is approaching. Our study can impact all efforts towards improving actively system resilience ranging from evaluation of design schemes, development of protection strategies to implementation of mitigation programs.

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Using relaxational dynamics to reduce network congestion

Cited by 8 publications

References 27 publications

Jamming in complex networks with degree correlation

Jamming in complex networks with degree correlation

Fluctuations of a surface relaxation model in interacting scale free networks

Spatial correlation analysis of cascading failures: Congestions and Blackouts

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