USA Flights

Transportation Network

The OpenFlights USA dataset represents airports as nodes and flight routes as edges. It is commonly used to study connectivity, robustness, and hub structure.

Figure 1: USA flights: subgraph snapshot (random sample of airports).

Load the graph

The dataset is provided as GraphML and edge lists under data/openflights.

import networkx as nx

G = nx.read_graphml("data/openflights/openflights_usa.graphml.gz")

Basic checks

Try these metrics:

num_nodes = G.number_of_nodes()
num_edges = G.number_of_edges()
avg_degree = sum(dict(G.degree()).values()) / num_nodes

print(num_nodes, num_edges, avg_degree)

Explore hubs

Compute the top airports by degree centrality.

import networkx as nx

centrality = nx.degree_centrality(G)
# TODO: sort and print top 10 airports
centrality = nx.degree_centrality(G)
top10 = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:10]
print(top10)

Questions to explore

  • Which airports act as hubs?
  • How does the network change if you remove the top hub?
  • Is the graph connected, or are there isolated components?