Explosive percolation in thresholded networks

@article{Hayasaka2015ExplosivePI,
  title={Explosive percolation in thresholded networks},
  author={Satoru Hayasaka},
  journal={Physica A-statistical Mechanics and Its Applications},
  year={2015},
  volume={451},
  pages={1-9}
}
  • S. Hayasaka
  • Published 31 March 2015
  • Computer Science
  • Physica A-statistical Mechanics and Its Applications

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References

SHOWING 1-10 OF 28 REFERENCES

Explosive Percolation in Random Networks

It is shown that incorporating a limited amount of choice in the classic Erdös-Rényi network formation model causes its percolation transition to become discontinuous.

Using explosive percolation in analysis of real-world networks.

It is shown with finite-size scaling that the university class, ordinary or explosive, of the resulting percolation transition depends on the structural properties of the network, as well as the number of unoccupied links considered for comparison in this procedure.

Explosive Percolation Is Continuous

A mathematical proof shows that in many models of the growth of network connectivity, phase transitions are continuous, although related models in which the number of nodes sampled may grow with the network size can indeed exhibit explosive percolation.

Construction and analysis of random networks with explosive percolation.

The underlying mechanism behind first-order phase transitions in random networks is described and tools that allow us to identify (and predict) when a random network will exhibit an explosive transition are developed.

Explosive percolation in scale-free networks.

The Achlioptas growth process leads to a phase transition with a nonvanishing percolation threshold already for lambda>lambda(c) approximately 2.2.2, but the transition is continuous when lambda<or=3 but becomes discontinuous when lambda>3.

Explosive percolation transition is actually continuous.

A representative model is considered which shows that the explosive percolation transition is actually a continuous, second order phase transition though with a uniquely small critical exponent of thePercolation cluster size.

Explosive percolation in the human protein homology network

The results indicate that the evolutionary-based process that shapes the topology of the H-PHN through duplication-divergence events may occur in sudden steps, similarly to what is seen in first-order phase transitions.

Percolation on sparse networks

Percolation is reformulate as a message passing process and the resulting equations can be used to calculate the size of the percolating cluster and the average cluster size, finding them to be highly accurate when compared with direct numerical simulations.

Predicting percolation thresholds in networks.

  • F. Radicchi
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2015
This study reveals that the inverse of the largest eigenvalue of the nonbacktracking matrix of the graph often provides a tight lower bound for true percolation threshold, but in more than 40% of the cases, this indicator is less predictive than the naive expectation value based solely on the moments of the degree distribution.

Explosive percolation: a numerical analysis.

A detailed numerical analysis of Achlioptas processes with product rule on various systems, including lattices, random networks á la Erdös-Rényi, and scale-free networks shows that all relevant percolation variables display power-law scaling, just as in continuous second-order phase transitions.