Network science represents systems, dependencies and actors as graphs. In this field it maps attack paths through networks, concentration of digital dependencies, and the coordination patterns that reveal information operations.
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 cyber and digital resilience and critical infrastructure, 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.
Attack path analysis
Identities, permissions and systems are modelled as a graph to find paths from exposed entry points to critical assets, and the links whose removal cuts most paths.
Digital dependency concentration
Dependencies of services on providers, libraries and infrastructure are mapped to find nodes on which many services depend.
Coordination detection
Accounts that post the same content at the same times are linked in graphs to identify coordinated networks, with thresholds and false positive checks stated.
03Geography
Connected activity in Europe.
Night lights, population density and true-colour imagery over Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
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.
Connected activity, framed on Europe.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
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
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
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 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
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
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
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
Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). 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).
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).
Optional layers (5 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
Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
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.
Data
Role
Label
Identity and permission data
Attack graphs
OBSERVED
Software dependency graphs
Concentration
OBSERVED
Platform data available to researchers
Coordination patterns
OBSERVED
05Monitor
Network analysis.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Network analysis
Cybersecurity · Network science for cybersecurity · Run R-1892
Single point of failureGCRI · Cybersecurity and Digital Resilience
Dependency graph
nodes: assets · edges: dependencies
Most central nodes
N3
0.34
6
Critical
N4
0.14
7
Normal
N5
0.11
6
Normal
N6
0.41
6
Normal
Cascade size under removal
Network analysis: the screen's structure, terms and states for Cybersecurity.Example names and illustrative values; a design, not a record or a live system.
06Validation
How results are checked.
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
Check
How it is done
Path validation
Attack paths are confirmed with controlled testing where permitted
Threshold sensitivity
Coordination detection is tested across thresholds
Ground truth comparison
Detected networks are compared with platform takedowns where available
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
07Simulator
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows Europe; the model itself is not tied to a place.
Controls
Assets per layer
18
Hazard footprint
8% of area
Assets that depend on another layer
60%
Spare capacity (tolerance)
30%
Readouts
Assets hit by the hazard
9%
Assets still working
22%
Amplification of the initial damage
8.4 times
Steps to settle
3
Numbers, methods and sources
Step
Data centres working (%)
Networks working (%)
Power working (%)
0
94
83
94
1
39
50
78
2
28
17
28
3
28
11
28
Methods
Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
Overload cascades Motter and Lai (2002), Physical Review E 66
Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
A cascade through interdependent networks. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Interdependent networks (Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464); Overload cascades (Motter and Lai (2002), Physical Review E 66); Betweenness load (Brandes (2001), Journal of Mathematical Sociology 25).
Model network science for cybersecurity as a system. Run the cascade.
Assembled from the Nexus Standards area profile Cyber and digital resilience law alignment: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
How network science for cybersecurity is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in network science for cybersecurity: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Causal loop diagram from the page's systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
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.
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.
GCRICybersecurity and Digital ResilienceStrategic SystemsScenario and Stress-Test
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