Epidemic spreading on complex networks with general degree and weight distributions

@article{Wang2014EpidemicSO,
  title={Epidemic spreading on complex networks with general degree and weight distributions},
  author={Wei Wang and Ming Tang and Haifeng Zhang and Hui Gao and Younghae Do and Zonghua Liu},
  journal={Physical review. E, Statistical, nonlinear, and soft matter physics},
  year={2014},
  volume={90 4},
  pages={
          042803
        }
}
  • Wei WangM. Tang Zonghua Liu
  • Published 2 July 2014
  • Computer Science
  • Physical review. E, Statistical, nonlinear, and soft matter physics
The spread of disease on complex networks has attracted wide attention in the physics community. Recent works have demonstrated that heterogeneous degree and weight distributions have a significant influence on the epidemic dynamics. In this study, a novel edge-weight-based compartmental approach is developed to estimate the epidemic threshold and epidemic size (final infected density) on networks with general degree and weight distributions, and a remarkable agreement with numerics is obtained… 

Figures from this paper

Epidemic threshold of node-weighted susceptible-infected-susceptible models on networks

In this paper, we investigate the epidemic spreading on random and regular networks through a pairwise-type model with a general transmission rate to evaluate the influence of the node-weight

Unification of theoretical approaches for epidemic spreading on complex networks

This short survey unifies the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean- field, the quench mean-fields, dynamical message-passing, link percolation, and pairwise approximation.

Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics

Simulation results on six real networks show that the epidemic prevalence can be largely promoted when strong ties are favored, and analysis suggests that the weight–weight correlation strongly affects the results: high-weight edges are more significant in keeping high epidemic prevalence when the weight-weight correlation is positive.

Large epidemic thresholds emerge in heterogeneous networks of heterogeneous nodes

It is shown that the resilience of networks with heterogeneous connectivity can surpass those of networksWith homogeneous connectivity, which implies that network heterogeneity should not be studied in isolation, it is instead the heterogeneity of infection risk that determines the likelihood of outbreaks.
...

References

SHOWING 1-10 OF 85 REFERENCES

New

Proc. Natl. Acad. Sci. USA 98

  • Proc. Natl. Acad. Sci. USA 98
  • 2001

Phys

  • Rev. E88, 022813
  • 2013

Phys

  • Lett. A 378, 635C640
  • 2014

51

45

Phys

  • Rev. E 80, 036105
  • 2009

Phys. Rev. E

  • Phys. Rev. E
  • 2009

Phys. Rev. E

  • Phys. Rev. E
  • 2013

Networks an introduction (Oxford press)

  • Networks an introduction (Oxford press)
  • 2010
...