A relatively simple model for percolation properties of real networks

Tao, Fu; Zou, Liling; Li, Chenguang; Zhao, Junbo

doi:10.1016/j.physleta.2017.06.005

Cited by 9 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first network US Power Grid has already been utilized by many researchers to examine their findings (e.g. [4][19][20], and in [4], the largest existing cluster was used as the indicator of the percolating cluster in numeral simulations). From Fig.…”

Section: Applications To Real Networkmentioning

confidence: 99%

“…Estimating the fraction of the percolating cluster when the occupation probability is above the percolation threshold is a fundamental component of many percolation analytic models or algorithms (e.g. [1][2][3] [4]). Among them, the message passing algorithm proposed by Karrer et al [5] [6] is pretty outstanding and discussed or further applied by many following papers (e.g.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Semi-Analytic Algorithm to Estimate Clusters With Loops in Percolation on Real Networks

Li,

Fu,

Wang

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Estimating the fraction of the percolating cluster is at the core of many percolation models. For real networks, the total size of clusters with loops can be considered a plausible metric for the percolating cluster. In this paper, we develop a semi-analytic algorithm to estimate clusters with loops for both site and bond percolation via modifying the message passing algorithm. We compared the estimates of the original message passing algorithm and our modified version with simulation results on four real networks. Our findings suggest that our modified algorithm can achieve high accuracy for any real network, provided that a sufficient number of possible states following site or bond occupation are selected and analyzed to calculate the final estimate.

show abstract

Section: Applications To Real Networkmentioning

confidence: 99%

mentioning

confidence: 99%

A Semi-Analytic Algorithm to Estimate Clusters With Loops in Percolation on Real Networks

Li,

Fu,

Wang

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…This structural approach to quantify the node vulnerability will be also used in this work to compare results of removal methods based on dynamical measurements. Other relevant works have to be mentioned; for instance, based on the statistical physics description of networks, some basic percolation properties for different networks have been analysed using generating functions [33], the lifetime and reliability of networks has been estimated by setting some probability distributions for node failures [34] and the propagation of cascading failures through the power transmission or communication architecture of smart grids has also been modeled [35]. The rest of this paper is organized as follows: Section 2 introduces the dynamical model of a power grid and the structure of the specific test cases, as well as some theoretical tools that will be applied in further analysis of synchronization and vulnerability measurements.…”

Section: Introductionmentioning

confidence: 99%

Decreased resilience in power grids under dynamically induced vulnerabilities

Galindo-González,

Angulo-García,

Osorio

2020

Preprint

View full text Add to dashboard Cite

In this paper, a methodology inspired on bond and site percolation methods is applied to the estimation of the resilience against failures in power grids. Our approach includes vulnerability measures with both dynamical and structural foundations as an attempt to find more insights about the relationships between topology and dynamics in the second-order Kuramoto model on complex networks. As test cases for numerical simulations, we use the real-world topology of the Colombian power transmission system, as well as randomly generated networks with spatial embedding. It is observed that, by focusing the attacks on those dynamical vulnerabilities, the power grid becomes, in general, more prone to reach a state of total blackout, which in the case of node removal procedures it is conditioned by the homogeneity of power distribution in the network.

show abstract

“…Many examples concern computer science and the Internet. Indeed, the birth of the Internet is just a percolation problem: a worldwide network arose from the sequential adding of links among nodes (computers) [15]. The resilience of the Internet to failure can be formulated as a percolation phase transition problem since its degree distribution implies the presence of heavily connected hubs [4].…”

Section: Introductionmentioning

confidence: 99%

Percolation and Internet Science

et al. 2019

View full text Add to dashboard Cite

Percolation, in its most general interpretation, refers to the “flow” of something (a physical agent, data or information) in a network, possibly accompanied by some nonlinear dynamical processes on the network nodes (sometimes denoted reaction–diffusion systems, voter or opinion formation models, etc.). Originated in the domain of theoretical and matter physics, it has many applications in epidemiology, sociology and, of course, computer and Internet sciences. In this review, we illustrate some aspects of percolation theory and its generalization, cellular automata and briefly discuss their relationship with equilibrium systems (Ising and Potts models). We present a model of opinion spreading, the role of the topology of the network to induce coherent oscillations and the influence (and advantages) of risk perception for stopping epidemics. The models and computational tools that are briefly presented here have an application to the filtering of tainted information in automatic trading. Finally, we introduce the open problem of controlling percolation and other processes on distributed systems.

show abstract

A relatively simple model for percolation properties of real networks

Cited by 9 publications

References 12 publications

A Semi-Analytic Algorithm to Estimate Clusters With Loops in Percolation on Real Networks

A Semi-Analytic Algorithm to Estimate Clusters With Loops in Percolation on Real Networks

Decreased resilience in power grids under dynamically induced vulnerabilities

Percolation and Internet Science

Contact Info

Product

Resources

About