Early Warning System

Discover how infrastructure networks naturally evolve to critical states where small operational changes trigger massive cascading failures

Powered by the Self-Organized Criticality Model

Step 1: Choose Infrastructure Network

🛩️ Hub-and-Spoke Airlines

Major hubs like Atlanta and Chicago concentrate most traffic. Highly efficient but vulnerable to cascade failures when key hubs fail.

30 airports High Risk

✈️ Point-to-Point Regional

Distributed connections avoid major hubs. More resilient to individual failures but can still experience regional cascades.

30 airports Medium Risk

🌐 Fragmented Regional

Multiple isolated regional clusters with minimal cross-connections. Most resilient to system-wide cascades.

30 airports Low Risk

SOC Simulation Running

Analyzing cascading failure dynamics...
Initializing simulation...

Analysis Dashboard

Advanced cascade failure analysis including conditional probabilities and sequence patterns.

Total Cascades Observed
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Number of cascading failure events during simulation
Average Cascade Size
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Mean number of nodes affected per cascade event
Maximum Cascade
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Worst-case cascade size observed
System Criticality
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Overall network vulnerability to cascades (0-100)
Unique Failure Sequences
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Number of distinct failure transition patterns
Average Sequence Length
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Mean number of failures per cascade sequence
Critical Nodes
Dangerous Sequences
Conditional Analysis
Risk Assessment
Network Statistics

Most Critical Infrastructure Nodes

These nodes have the highest risk of triggering or participating in cascade failures. Monitor these closely for early warning signs.

Most Cascade-Prone Nodes

Nodes frequently involved in cascading failure events.

Most Avalanche-Triggering Nodes

Nodes that most frequently initiate cascading failures.

Most Dangerous Failure Sequences

P(Node B fails | Node A just failed) - Highest conditional probabilities

Most Frequent Transitions

Failure transitions observed most often during simulation

Hub-Specific Failure Impact Analysis

When major hubs fail, which airports are most likely to experience secondary failures?

Highest Outgoing Risk

Nodes most likely to trigger failures in other nodes

Highest Incoming Risk

Nodes most vulnerable to secondary failures

System Resilience Metrics

Failure Distribution Analysis

Early Warning Effectiveness

Network Topology Properties

Simulation Parameters Used

Performance Statistics