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GRF · Information Integrity and Public Trust

Network science and contagion for information integrity and public trust

Network science studies how connections between accounts, outlets and communities shape the spread of information. In information integrity it identifies coordinated behaviour, maps how narratives travel and shows where interventions on networks would change reach.

01Register

Key facts.

Method recordSTEM method
  1. MethodNetwork science and contagion
  2. Applied inInformation Integrity and Public Trust
  3. Canonical pageSTEM Commons
02Application

What network science and contagion does here.

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. In information and public trust and society and human development, network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades. The canonical description is on the STEM Commons.

Detecting coordinated behaviour

Accounts that post the same content at the same times or share links in synchronised patterns are identified, with thresholds and false positive checks stated.

Tracing narrative spread

Diffusion cascades show how a claim moved between communities and platforms, identifying the accounts and outlets that bridged them.

Mapping media ecosystems

Hyperlink and citation networks between outlets show clusters and isolated communities in a country's media system.

03Geography

People and exposure in Eastern Europe.

Population density, night lights and land cover over Eastern Europe, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

People and exposure, for this page

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

People and exposure, framed on Eastern Europe.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · CIESIN/Columbia, NASA SEDAC HBASE · U.S. Geological SurveySources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
  • Live: U.S. Geological Survey Events are indicative, not authority warnings.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
  • Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
  • Optional layers (9 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026

04Data

What the method consumes.

Datasets and records used by network science and contagion on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DataRoleLabel
Platform research APIs and data accessNetwork dataOBSERVED
Hyperlink and citation dataOutlet networksOBSERVED
Platform takedown datasetsKnown coordinated networksREPORTED
Fact-check databasesLabelled claimsREPORTED
05Decision model

What buffers and exposure limits change.

How leverage limits, exposure caps and recovery arrangements change how far a shock spreads through the banking system, the levers supervisors and central banks hold. Teaching model with stated illustrative values, not a prediction. The map on this page shows Eastern Europe; the model itself is not tied to a place.

Controls

Fall in external assets
2.0%
Leverage (assets over equity)
10 times
Interbank share of assets
10%
Recovery on interbank claims
40%
Banks in the system
20
Core banks
20% of banks

Readouts

Equity lost to the initial shock
18.0%
Equity lost after contagion
45.0%
Added by contagion (DebtRank)
27.0 points
Banks in default
0
Numbers, methods and sources
RoundSystem equity lost (%)Banks in default
018.000
128.800
235.280
339.170
441.500
542.900
643.740
744.240
844.550
944.730
1044.840
1144.900
1244.940
1344.960
1444.980
1544.990
1644.990
1745.000
1845.000
1945.000
2045.000
2145.000
2245.000
2345.000
2445.000
2545.000
2645.000
2745.000
2845.000
2945.000
3045.000

Methods

  1. DebtRank Battiston and others (2012), Scientific Reports 2:541
  2. Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
  3. Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
  4. Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
  5. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  6. One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.

What buffers and exposure limits change. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: DebtRank (Battiston and others (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia and others (2015), PLOS ONE 10); Contagion in financial networks (Gai and Kapadia (2010), Proc. R. Soc. A 466).
06Validation

How results are checked.

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

CheckHow it is done
Coordination is not causationCoordinated patterns are distinguished from organic convergence on a topic
Data access biasThe platform and period covered by the data are stated
RobustnessNetwork findings are tested across thresholds and samples

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

07Twin and cascade simulator

Exercise the cascade with the people who decide.

Assembled from the Nexus Standards area profile Information integrity and trust: the hazards it names, the systems and communities that carry the loss, the controls and institutions that dampen it and the data that observe it. Pick an initiating event and step through the cascade to see where mandates meet and which decisions fall to whom. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise for dialogue, not a forecast, an assessment or a position. To model a place of your own choosing, open the full System Builder.

Hazards

  • Civil unrest
  • Ice-jam flood
  • Tropical cyclone
  • Heatwave
  • HIV epidemic
  • Dam failure
  • Storm surge (set off downstream)
  • Intense rainfall (set off downstream)
  • River flood (set off downstream)
  • Flash flood (set off downstream)
  • Dangerous surf (set off downstream)
  • Widespread power outage (set off downstream)
  • Mass displacement (set off downstream)
  • Coastal flood (set off downstream)
  • Extreme fire weather (set off downstream)
  • Wildfire (set off downstream)
  • Heat-driven grid stress (set off downstream)
  • Agricultural drought (set off downstream)

Controls

  • NS-0 Constitution and principles
  • NS-2 Citation and attribution format for UNIs
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NS-12 Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)
  • NRS-H52 Hazard family: Information-integrity attacks (NHS-1 cyber.integrity)
  • NRS-X7 Information integrity and public trust
  • NRS-U23 Universe platform profile: Reports
  • NRS-A4 Zero-trust verification of standards and conformance
  • NRS-H65 Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)
  • NRS-H72 Hazard family: Novel entities and emerging contaminants (NHS-1 poll.novel-entities)

Observation

  • GDELT event database and global knowledge graph (listed for reference)
  • Our World in Data datasets (listed for reference)
  • Number of countries that adopt and implement guarantees for public access to information

Example geography

  • The Horn of Africa: 253 hexagonal cells
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Surface air temperature, daytime (AIRS), NASA GIBS
09Instruments and commitments

Instruments, mandates and commitments.

The instruments, mandates, actors and accountability arrangements a policymaker, diplomat or development agency works with, citing the agreements that set them. Structures follow the frameworks named. Any state or progress drawn is illustrative, not a record.

Accountability cycle

How commitments on network science and contagion for information integrity and public trust are held to account, from commitment to correction.

  1. CommitGovernmentA commitment with a mandate and a legal basis.
  2. PlanLead agencyA national plan with owners and milestones.
  3. ResourceMinistry of financeA budget line or financing on record.
  4. ImplementAgencies and local governmentDelivery against the plan.
  5. ReportStatistics officeIndicators reported in agreed definitions.
  6. ReviewLegislature, audit, civil societyIndependent review of results and gaps.
  7. CorrectGovernmentChanges to the plan, published with reasons.
A generic accountability cycle; the owner of each step follows the country's own constitution and law.
Sendai Framework for Disaster Risk Reduction · UNDRRParis Agreement · UNFCCC2030 Agenda and the Sustainable Development Goals · United NationsInternational Health Regulations · WHO
10Stewardship

Who maintains the method.

Systems Science and Modelling stewards network science and contagion for the Consortium and reviews how it is applied across platforms.

12Questions

Questions and answers.

What are the limits of network science and contagion in this field?
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
How are results checked?
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute

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Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

Global Risks ForumInformation Integrity and Public TrustEvidence Brief
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