Epidemic Spreading in Scale-Free Networks

Romualdo Pastor-Satorras, Alessandro Vespignani · Physical Review Letters / arXiv · 2001 · epidemiology · 10.1103/PhysRevLett.86.3200 · record pastorsatorras2001-epidemic-scalefree
tag layer · index · concepts · authors · עברית
Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. limitations left empty: the paper states no limits on its own results; the nearest thing is a claim of robustness (finite-size effects excluded, generalisable to 2 < gamma <= 3), which is the opposite of a limitation. Only one claim_relation recorded: the paper takes its scale-free graph from 'the algorithm devised in Refs. [6]' and its data from 'the Virus Bulletin [16]', but these targets appear as bare reference numbers with no author or work named in the running text, so per the rules they were not turned into relations. Method is really twofold (large-scale simulation plus a mean-field analytic solution); the facet records the simulation. The extraction garbles all mathematics (e.g. 'hk2i', 'ki/ Pj kj') and splices page numbers into the middle of sentences ('Quotation withheld'), so quotes were taken only from unbroken prose stretches. 2026-08-12 and 2026-08-15 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node. Update, 16 September 2026: EXCEPTION TO THE DATED PASS ABOVE, on this record only: this record carries 1 composite quote field - a quote assembled from two or more passages joined by an ellipsis. A COMPOSITE IS NOT AN EXACT CONTIGUOUS SUBSTRING of the staged text and is deliberately not anchored: each of its parts is verified separately at the offsets in evidence composite spans, and a part occurring more than once carries span null rather than a guessed offset. So the 'every quote verified as an exact substring' clause of the dated pass above DOES NOT HOLD for those fields, and never did - the parts were verified, the whole was not. Nothing is deleted, re-grounded or withdrawn: this writes down an exception that was always true of composites and had not been recorded, so a reader was told more than the pass had checked. 2026-09-16 the project CORRECTION TO THE EXEMPTION ABOVE, SAME DAY: THE EXEMPTION DOES NOT APPLY TO THIS RECORD. Its evidence composite spans row(s) are FOSSILS of a 2026-08-25 composite marking that was superseded by a 2026-08-26 re-slice; the quote is an exact contiguous substring of the staged text, so the dated substring claim above holds for it WITHOUT exception. The clause is left in place with this correction beside it rather than removed, so that the error and its repair are both on the record.
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Other fields define these differently the same word, defined another way in another discipline
threshold 2 fields · connectivity 2 fields · simulation 2 fields · statistical-significance 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 16 concept tags on this paper, 12 are grounded in a quoted sentence. 11 carry the tagger's gloss of what the term means in this paper, which is not something the article can license.
scale-free network epidemic threshold contact network degree distribution basic reproduction number scale-free networks node individual link susceptible-infected-susceptible contact process Euclidean lattice random networks Internet surviving probability prevalence the scale-free network diverging connectivity fluctuations remove the epidemic threshold, so infections persist for any positive transmission rate diverging connectivity fluctuations act as an infinite connectivity, and the threshold falls to zero a link is more likely to land on a highly connected node, so hubs are infected first and keep re-infecting the rest e-mail attachments make the contact graph the Internet's topology rather than physical proximity epidemic network scale-free spreading threshold virus power-law infection connectivity computer viruses mean-field model world-wide-web epidemic threshold λc effective spreading rate λ spreading rate λ virus prevalence statistical-significance statistically significant mean exponential-distribution simulation

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second readingalternativesan independent re-reading of a facet, recorded whether or not it agrees
relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
contributioncontributionwhat the paper claims to add
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
object of studyobject_of_studywhat the paper is about — the thing it investigates
phenomenonphenomenonthe effect or regularity under study
research questionresearch_questionthe question the paper sets itself
sensesensesa distinct use of a term, quoted where the paper fixes it
transfertransfersa method or concept carried in from another field
unrecordable relationunhoused_relationsa relation the paper asserts that this vocabulary cannot express, with the reason

The paper

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Every tag on this page

alternatives 3 · claim_relations 6 · claim_type 1 · concept_edges 3 · concepts 12 · content_tags 25 · contribution 1 · controls 1 · limitations 4 · mechanisms 4 · method 1 · object_of_study 1 · phenomenon 1 · research_question 1 · senses 3 · transfers 2 · unhoused_relations 1

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 3 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
object_of_studyThe spread of computer viruses on the Internet, treated as an epidemic process running on a scale-free network.
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object_of_studythe paper's words
research_questionWhy do computer viruses persist for years at very low prevalence, when standard epidemic models say an infection either dies out exponentially or takes off?
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research_questionthe paper's words
contributionShows that on a scale-free network the epidemic threshold vanishes, so an infection persists at any spreading rate - which explains the observed long-lived, low-prevalence computer viruses without tuning anything.
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contributionthe paper's words
claim_typemechanism
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claim_typethe paper's words
phenomenonepidemic spreading on scale-free networks
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phenomenonthe paper's words
methodsimulation
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also in: simulation
methodthe paper's words
mechanismsmechanisms
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mechanisms[0]the tagger's words
mechanismsmechanisms
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mechanisms[1]1.0the tagger's words
mechanismsmechanisms
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mechanisms[2]0.9the tagger's words
mechanismsmechanisms
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mechanisms[3]0.9the tagger's words
limitationsthe SIS model reduces individuals to two discrete states (healthy/infected)
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what this limits: every result of the SIS model: INDIVIDUALS EXIST ONLY IN TWO DISCRETE STATES
limitations[0]the tagger's words
limitationsThe model is run at one exponent, γ = 3, and the generalisation to other exponents is asserted qualitatively rather than shown.
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what this limits: the range of exponents the conclusion covers: THE PRESENT FRAMEWORK CAN BE GENERALIZED TO NETWORKS WITH 2 < gamma <= 3, RECOVERING QUALITATIVELY THE SAME RESULTS - asserted qualitatively, by citation
limitations[1]0.9the tagger's words
limitationsIndividuals are reduced to two states, healthy or infected, which the paper flags as a coarse description.
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what this limits: the same scope as limitations[0], in the paper's own words: THIS MODEL RELIES ON A COARSE GRAINED DESCRIPTION OF INDIVIDUALS IN THE POPULATION
limitations[2]1.0the tagger's words
limitationsThe empirical half rests on one industry data source over a four-year window.
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what this limits: the empirical half only: WE HAVE ANALYZED THE PREVALENCE DATA REPORTED BY THE VIRUS BULLETIN FROM FEBRUARY 1996 TO MARCH 2000 - one industry source, four years
limitations[3]0.8the tagger's words
transfersmodelling epidemic spreading on scale-free contact networks
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transfers[0]the tagger's words
transfersthe susceptible-infected-susceptible model and the notion of an epidemic threshold, applied to computer virus outbreaks on the Internet
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transfers[1]1.0the tagger's words
controlstesting the model's prediction against independent real-world prevalence data reported by the Virus Bulletin, February 1996 to March 2000
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eliminates: that the absence of an epidemic threshold is an artefact of the scale-free model rather than a property of how real computer viruses spread · outcome: held
controls[0]0.85the tagger's words
conceptsepidemic threshold
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concepts[1]the paper's words
conceptsscale-free networks
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concepts[5]1.0the paper's words
conceptsnode
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also in: node
concepts[6]1.0the paper's words
conceptsindividual
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also in: individual
concepts[7]1.0the paper's words
conceptslink
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also in: link
concepts[8]1.0the paper's words
conceptssusceptible-infected-susceptible
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concepts[9]1.0the paper's words
conceptscontact process
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concepts[10]1.0the paper's words
conceptsEuclidean lattice
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concepts[11]1.0the paper's words
conceptsrandom networks
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concepts[12]1.0the paper's words
conceptsInternet
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also in: Internet
concepts[13]1.0the paper's words
conceptssurviving probability
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concepts[14]1.0the paper's words
conceptsprevalence
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also in: prevalence
concepts[15]1.0the paper's words
unhoused_relationsEuclidean lattice → epidemic threshold
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A NONZERO EPIDEMIC THRESHOLD IS A PROPERTY OF THE LOCAL-CONNECTIVITY REGIME, NOT SOMETHING THE LATTICE PRODUCES. The lattice does not increase, reduce or eliminate a threshold; models with local connectivity simply HAVE one, and that is the standard result this paper is written against. None of the eight types says 'X holds in regime Y', and none of the four effect directions says a quantity EXISTS - all four describe a change in it.
unhoused_relations[0]0.95the tagger's words
sensesnetwork - social, biological and communication systems alike, whose nodes are individuals or organizations and whose links are the interactions among them
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senses[0]1.0the tagger's words
sensesnetwork - the Internet as the contact graph along which an e-mail-borne infection actually travels, as opposed to physical proximity
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senses[1]1.0the tagger's words
sensesepidemic - a computer virus outbreak, analysed with the models of biological epidemiology
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senses[2]1.0the tagger's words
alternativesclaim_type: mechanism
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a second reading reaching the same value. Recorded: mechanism — note: a second reading reaching the same value from a different sentence. Not applied - a person decides.
alternatives.claim_typethe tagger's words
alternativesmethod: simulation
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a second reading reaching the same value. Recorded: simulation — note: a second reading reaching the same value from a different sentence. Not applied - a person decides.
alternatives.methodthe tagger's words
alternativesphenomenon: absence of an epidemic threshold for spreading on scale-free networks
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a second reading DIFFERING from the recorded value. Recorded: epidemic spreading on scale-free networks — note: a second reading, verbatim-verified, disagreeing with the value on the record. Not applied - a person decides.
alternatives.phenomenonthe tagger's words
claim_relationsbuilds-on → scale-free network model
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claim_relations[0]the tagger's words
claim_relationscontradicts → the classical nonzero-epidemic-threshold prediction
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claim_relations[1]the tagger's words
claim_relationscontradicts → the standard nonzero epidemic threshold of mathematical epidemiology [11]
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claim_relations[2]the tagger's words
claim_relationsbuilds-on → the scale-free graph model of Barabasi and Albert [6]
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claim_relations[3]1.0the tagger's words
claim_relationscontradicts → standard epidemic modelling on lattices and mean-field models, which predicts a nonzero threshold
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differs in: THE AXIS IS THE CONNECTIVITY REGIME. 'Quotation withheld.' The threshold is not wrong for the models it was derived in - it is a property OF THOSE MODELS' CONNECTIVITY, and this paper changes the connectivity. Its unhoused_relations[0] in this corpus records the same point as a relation the vocabulary could not express: a threshold is a property of a regime, not something a lattice produces.
claim_relations[4]1.0the tagger's words
claim_relationsextends → the observational literature on computer virus prevalence [10,13]
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claim_relations[5]1.0the tagger's words
concept_edgesscale-free networks —contrasted-with→ random networks
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strength of the relation: 1 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but endpoint by anaphora: 'That' = the scale-free connectivity of the Internet (previous sentence).
concept_edges[2]the tagger's words
concept_edgescontact process —is-a→ susceptible-infected-susceptible
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strength of the relation: 0.9 Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span says the SIS model is a GENERALIZATION of the CP model; is-a is loose, endpoints abbreviated.
concept_edges[3]the tagger's words
concept_edgesEVIDENCE CORRECTED (RE-GROUNDED) · scale-free networks —causes→ epidemic threshold
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EVIDENCE CORRECTED: RE-GROUNDED. The old span - 'annulling thus the threshold' - states the mechanism and names neither endpoint. This sentence names both AND states the finding, and the sign travels with it. — direction on the measure: eliminates · strength of the relation: 1
concept_edges[4]the tagger's words
content_tagsγ between 2 and 3 — power-law degree exponent of the scale-free networks (Internet/WWW)
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content_tags.quantities[0]0.85the tagger's words
content_tags0 (no threshold) — the epidemic threshold vanishes for scale-free networks
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content_tags.quantities[1]the tagger's words
content_tagsτ ≃ 14 months — characteristic lifetime of boot and macro virus strains
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content_tags.quantities[2]1.0the tagger's words
content_tagsλc = 0 — the epidemic threshold on a scale-free network
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content_tags.quantities[3]1.0the tagger's words
content_tagsN = 103 to N = 8.5 × 106 — range of simulated network sizes (the exponents are lost in extraction; the paper means 10^3 to 8.5x10^6)
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about this quote: FLATTENED EXPONENT IN THIS QUOTE - THE NUMBER IS WRONG BY ORDERS OF MAGNITUDE AS IT STANDS. The staged text reads '8.5 × 106', where the paper writes 10 to the power 6. The superscript was pushed onto the line by the PDF extractor. The quote is verbatim to the staged text and its anchor is sound; the STAGED TEXT is what is damaged. NOT repaired - repair means re-staging the body and re-anchoring the record.
content_tags.quantities[4]0.9the tagger's words
content_tagsγ ≃ 2.5 — measured scale-free exponent of the Internet and the world-wide-web
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content_tags.quantities[5]1.0the tagger's words
content_tags100 starting configurations x 10 network realisations — averaging used for every prevalence figure
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content_tags.quantities[6]1.0the tagger's words
content_tagsan epidemic threshold and its associated critical behavior are absent
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content_tags.findings[0]the tagger's words
content_tagsReal computer-virus data are analysed to give the average lifetime and prevalence of viral strains on the Internet.
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content_tags.findings[1]the tagger's words
content_tagsOn a scale-free network the epidemic threshold is zero: any spreading rate, however small, lets the infection persist.
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content_tags.findings[2]1.0the tagger's words
content_tagsSteady-state virus prevalence decays with decreasing spreading rate as ρ ~ exp(−C/λ).
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content_tags.findings[3]1.0the tagger's words
content_tagsFitting the data gives a lifetime of about 14 months for boot and macro viruses and 6-9 months for file viruses.
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content_tags.findings[4]1.0the tagger's words
content_tagsThe higher a node's connectivity, the higher its probability of being infected.
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content_tags.findings[5]1.0the tagger's words
content_tagsspreading rate λ
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content_tags.variables[0]the tagger's words
content_tagseffective spreading rate λ
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content_tags.variables[2]1.0the tagger's words
content_tagsvirus prevalence
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content_tags.variables[3]1.0the tagger's words
content_tagsepidemic threshold λc
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content_tags.definitions[0]the tagger's words
content_tagsscale-free network
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content_tags.definitions[1]the tagger's words
content_tagssusceptible-infected-susceptible
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content_tags.definitions[2]1.0the tagger's words
content_tagseffective spreading rate λ
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content_tags.definitions[3]1.0the tagger's words
content_tagsepidemic threshold
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content_tags.definitions[4]1.0the tagger's words
content_tagsstatistical-significance
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content_tags.statistics[0]the tagger's words
content_tagspower-law
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also in: power-law
content_tags.statistics[1]1.0the tagger's words
content_tagsmean
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also in: mean
content_tags.statistics[2]0.9the tagger's words
content_tagsexponential-distribution
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content_tags.statistics[3]1.0the tagger's words

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