Using Aura Graph Analytics to Model NYC Subway Disruptions

Powerful Network Modeling with Aura Graph Analytics. In this short video, we’ll explore how to simulate real-world disruptions—like a closed NYC subway station—using Aura Graph Analytics
We’ll walk through an example where the NYC subway is modeled as a graph of stations and connections, and then subjected to pathfinding analysis using Dijkstra’s algorithm. We show how a single station closure can ripple through the network and how Aura Graph Analytics can quickly identify alternative routes.
Using Neo4j Aura Graph Analytics capabilities, you’ll see how to:
• How to import and clean transit data
• How to create directed graph projections
• How to run Dijkstra’s shortest path algorithm to calculate least-hop or least-cost routes
• How to simulate disruptions (like a station closure) by excluding specific nodes from your projection
• How this approach scales to real-world applications like supply chain resilience, manufacturing flow, and logistics optimization
• Run graph algorithms at scale — twice as fast as open-source tools

Explore Neo4j Graph Data Science → https://neo4j.com/product/aura-graph-analytics
Learn more about Neo4j AuraDB → https://neo4j.com/cloud/aura
Access the code and dataset on GitHub → https://github.com/neo4j-product-examples/aura-graph-analytics