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Network science and contagion

Network science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease. It provides tools to analyse interdependent infrastructure, supply chains and financial systems. Systems Science and Modelling stewards this page for the Nexus Consortium.

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01Workspace

The workspace for network science and contagion.

Network analysis

The Global Centre for Risk and Innovation (GCRI) · Network science and contagion · Run R-3937 · Example interface, not a record

Single point of failureGCRI · GCRI

Dependency graph

nodes: assets · edges: dependencies

Most central nodes

N30.195Critical
N40.1612Normal
N50.377Normal
N60.3612Normal

Cascade size under removal

Network analysis.Illustrative interface; not live data.

GCRI · Network analysis. Network analysis: example names and illustrative values, not a record.

02The method

What network science and contagion covers.

  1. 01

    Core methods

    graph analysis; centrality measures; cascade and contagion modelling; multilayer network analysis; percolation analysis; community detection.

  2. 02

    Data and instruments

    infrastructure topology data; trade and supply chain links; interbank exposure data; contact and mobility data; communication networks.

  3. 03

    Validation

    Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.

  4. 04

    Limits

    Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.

  5. 05

    Use in risk decisions

    Network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades.

03Reference practice

Network methods are used in supervision, infrastructure and epidemiology.

ReferenceWhat it sets
Interbank network analysis in supervisory practiceContagion in financial systems
Infrastructure interdependency modellingCascading failure
Contact network models in epidemiologyTransmission
04Process

How the process runs.

Process

Network science and contagion: Cascade across systems

An initial hazard can trigger later risks and events; this illustration draws one cascade across systems such as energy, water, ICT, health and transport.

  1. 01 Hazard

    1. 01Initiating hazardDataset · Independent reviewerCascading risk is the risk that hazard events trigger later risks and events, through chains of interrelated hazards or impacts.

  2. 02 First-order systems

    1. 02Example system: energyModel · System operatorAn example of a system a hazard can hit first. What depends on it is set by a dependency survey.
    2. 03Example system: waterModel · System operatorA second example of a system a hazard can hit first. Its links to other systems are shown as examples only.

  3. 03 Second-order systems

    1. 04Example system: ICTModel · System operatorAn example of a system that may depend on others, for example on backup power. Survey data set the real dependencies.
    2. 05Example system: health servicesModel · System operatorAn example of a system that may depend on water, power and data. Survey data set the real dependencies.
    3. 06Example system: transportModel · System operatorAn example of a system that may depend on ICT, fuel and logistics. Survey data set the real dependencies.

  4. 04 Limiting the cascade

    1. 07Buffers and redundancyStep · Independent reviewerBuffers, redundancy and restoration can limit how far an impact spreads. The OECD report moves from single assets to system resilience.
    2. 08Restoration prioritiesDecision · Independent reviewerThe responsible party sets which services are restored first, under its own rules.

Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval. The dependencies are examples. A dependency survey for the place sets them.

05In systemic risk work

Contagion through links.

Network science models how failures propagate along dependencies. Results are sensitive to network data quality: missing links can hide contagion paths and spurious links can exaggerate them. Multilayer networks represent dependencies across systems.

  • Report network data completeness.
  • Use multilayer models for cross-system dependencies.
  • Test sensitivity to missing links.
  • Applications on Systems Modelling and Trade and Strategic Resources.
06Applied in

Platforms that apply this method.

07Stewardship

Who maintains this page.

Systems Science and Modelling stewards network science and contagion: it keeps this description current and reviews how the method is applied across the platforms.

08Sources

Where this page’s references lead.

The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.

09Questions

Questions and answers.

Why does one institution host every method page?
GCRI hosts the STEM Commons so that each method has one canonical description. Platforms link to it rather than repeat it, which keeps definitions consistent.
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Write to the integrity concern form with the passage and the reason.
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