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.
Supported in partVerified against the work's full text
The paper supports spreading rate, network topology and an epidemic threshold as the decisive factors, but its headline result is that scale-free topology removes the threshold.
“If the value of λ is above the threshold, λ ≥ λc, the infection spreads and becomes persistent. Below it, λ < λ c, the infection dies out exponentially fast.”
- 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
- In epidemiology, Pastor-Satorras and Vespignani's (2001) analysis
Supported in partVerified against the work's full text
The paper is Pastor-Satorras and Vespignani's 2001 epidemiological analysis of spreading on scale-free networks, with no epidemic threshold.
“We define a dynamical model for the spreading of infections on scale-free networks, finding the absence of an epidemic threshold and its associated critical behavior.”
- In epidemiology, Pastor-Satorras and Vespignani's (2001) analysis
- 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.
SupportedVerified against the work's full text
Pastor-Satorras and Vespignani find no epidemic threshold on scale-free networks: infections persist at any finite spreading rate, driven by highly connected nodes.
“This indicates that infections can proliferate on these scale-free networks whatever spre ading rates they may have.”
- 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.
Supported in partVerified against the work's full text
Shows hubs dominate infection on scale-free networks (no epidemic threshold); it does not compare sparse and dense graphs or give propagation timescales.
“the higher the node connectivity, the higher t he probability to be infected.”
- 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.
Supported in partVerified against the work's full text
Supports that scale-free connectivity (not just average connectivity) drives the epidemic behaviour, notably absence of a threshold, but in computer-virus/Internet networks and without the phrase 'scale-free dynamics'.
“The emerging picture for epidemic spreading in complex netw orks emphasizes the role of topology in epidemic modeling.”
- Scale-free contact networks predict scale-free epidemic dynamics because the degree-distribution signature governs the spreading law.
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 5 of 7 citations: 1 supported, 4 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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See how references were verified.
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