Epidemic Spreading in Scale-Free Networks.¶
Pastor-Satorras, R., & Vespignani, A. (2001). Epidemic Spreading in Scale-Free Networks. Physical Review Letters, 86(14), 3200-3203.
Cited by¶
7 citations across 7 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Cascade
- Percolation and firebreak intuitions developed in grid-failure analysis transfer directly to financial-contagion stress testing and to epidemic control, where the corresponding moves are isolating sub-networks, quarantining, and raising vaccination coverage above the percolation threshold.
This sourceShows epidemic propagation depends on contact-network topology — notably the absence of an epidemic threshold in scale-free networks — supplying the contact-network models that transfer to financial stress testing and grid cascade analysis with shared super-spreader, coupling-strength, and firebreak questions.
- Percolation and firebreak intuitions developed in grid-failure analysis transfer directly to financial-contagion stress testing and to epidemic control, where the corresponding moves are isolating sub-networks, quarantining, and raising vaccination coverage above the percolation threshold.
- Contagion
- Naming contagion foregrounds the two determinants that actually decide spread: the transmission mechanism (how readily the state jumps per contact) and the contact topology (who is linked to whom), with the reproduction threshold as the switch between containment and epidemic.
This sourceFoundational result that epidemic propagation depends on contact-network topology (e.g., absence of an epidemic threshold in scale-free networks), supplying the contact-network models that transfer to financial stress testing and grid cascade analysis with shared super-spreader, coupling-strength, and firebreak questions.
- Naming contagion foregrounds the two determinants that actually decide spread: the transmission mechanism (how readily the state jumps per contact) and the contact topology (who is linked to whom), with the reproduction threshold as the switch between containment and epidemic.
- Network
- Percolation
- The frame's distinctive payoff is that network heterogeneity changes the calculus: on a contact network with a few high-degree hubs (super-spreaders), the threshold can be vanishingly small, so even sparse contact suffices for system-spanning spread.
This sourceShows the epidemic threshold can vanish on heterogeneous (scale-free) contact networks, so even sparse contact spans and hub targeting is efficient.
- The frame's distinctive payoff is that network heterogeneity changes the calculus: on a contact network with a few high-degree hubs (super-spreaders), the threshold can be vanishingly small, so even sparse contact suffices for system-spanning spread.
- Response-vs-Propagation Race
- Propagation on sparse graphs has a slow effective timescale; propagation on dense or scale-free graphs has a very fast one, especially through hubs.
This sourceDemonstrates that propagation on scale-free graphs is dominated by hubs, drastically lowering the effective epidemic threshold.
- Propagation on sparse graphs has a slow effective timescale; propagation on dense or scale-free graphs has a very fast one, especially through hubs.
- Systemic Risk
- The epidemiologist's contact-network model of contagion transfers directly to financial-network stress testing and to power-grid cascade analysis: in each, the key questions are the same — which nodes are super-spreaders, how does coupling strength gate propagation, and where should firebreaks be placed.
This sourceFoundational result that epidemic propagation depends on contact-network topology (e.g., absence of an epidemic threshold in scale-free networks), supplying the contact-network models that transfer to financial stress testing and grid cascade analysis with shared super-spreader, coupling-strength, and firebreak questions.
- The epidemiologist's contact-network model of contagion transfers directly to financial-network stress testing and to power-grid cascade analysis: in each, the key questions are the same — which nodes are super-spreaders, how does coupling strength gate propagation, and where should firebreaks be placed.
- Universality
- Scale-free contact networks predict scale-free epidemic dynamics because the degree-distribution signature governs the spreading law.
This sourceProves scale-free contact networks yield a vanishing epidemic threshold, the spreading law governed by the degree-distribution signature rather than the pathogen.
- Scale-free contact networks predict scale-free epidemic dynamics because the degree-distribution signature governs the spreading law.
Verification¶
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