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Analysis and Resilience of the U.S. Flight Network

Sushrit Kafle, Shreejan Pandey

TL;DR

This work analyzes the U.S. Flight Network (USFN) with complex network theory to assess efficiency and vulnerability. By constructing a weighted, undirected USFN from a global air transportation dataset, it characterizes topology, community structure, and hub roles. The study reveals a hub-dominant, scale-free-like degree distribution (gamma ≈ 1.89) with meaningful geographic communities, and shows resilience to random failures but high fragility under targeted hub attacks, with a critical breakdown threshold between 0.15 and 0.20. The findings highlight the importance of protecting major hubs and point to future directions in weighted analyses, optimization, and multi-modal integration to enhance robustness and resilience.

Abstract

Air travel is one of the most widely used transportation services in the United States. This paper analyzes the U.S. Flight Network (USFN) using complex network theory by exploring how the network's topology contributes to its efficiency and vulnerability. This is done by examining the structural properties, degree distributions, and community structures in the network. USFN was observed to follow power-law distribution and falls under the anomalous regime, suggesting that the network is hub dominant. Compared to null networks, USFN has a higher clustering coefficient and modularity. Various percolation test revealed that USFN is vulnerable to targeted attacks and is susceptible to complete cascading failure if one of the major hubs fails. The overall results suggest that while the USFN is designed for efficiency, it is highly vulnerable to disruptions. Protecting key hub airports is important to make the network more robust and prevent large-scale failures.

Analysis and Resilience of the U.S. Flight Network

TL;DR

This work analyzes the U.S. Flight Network (USFN) with complex network theory to assess efficiency and vulnerability. By constructing a weighted, undirected USFN from a global air transportation dataset, it characterizes topology, community structure, and hub roles. The study reveals a hub-dominant, scale-free-like degree distribution (gamma ≈ 1.89) with meaningful geographic communities, and shows resilience to random failures but high fragility under targeted hub attacks, with a critical breakdown threshold between 0.15 and 0.20. The findings highlight the importance of protecting major hubs and point to future directions in weighted analyses, optimization, and multi-modal integration to enhance robustness and resilience.

Abstract

Air travel is one of the most widely used transportation services in the United States. This paper analyzes the U.S. Flight Network (USFN) using complex network theory by exploring how the network's topology contributes to its efficiency and vulnerability. This is done by examining the structural properties, degree distributions, and community structures in the network. USFN was observed to follow power-law distribution and falls under the anomalous regime, suggesting that the network is hub dominant. Compared to null networks, USFN has a higher clustering coefficient and modularity. Various percolation test revealed that USFN is vulnerable to targeted attacks and is susceptible to complete cascading failure if one of the major hubs fails. The overall results suggest that while the USFN is designed for efficiency, it is highly vulnerable to disruptions. Protecting key hub airports is important to make the network more robust and prevent large-scale failures.
Paper Structure (23 sections, 3 equations, 14 figures, 4 tables)

This paper contains 23 sections, 3 equations, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Data and Network Construction
  3. Dataset
  4. Data Filtering
  5. Network Construction
  6. USFN Network Property
  7. Nodes and Edges
  8. Average Shortest Path Length
  9. Network Diameter
  10. Degree Distribution
  11. Betweenness Centrality
  12. Network Analysis
  13. Power-Law Fitting of the Degree Distribution
  14. Community Structure
  15. Comparison with Null Models
  16. ...and 8 more sections

Figures (14)

  • Figure 1: Degree distribution of the US Flight Network.
  • Figure 2: Betweenness centrality of nodes (airports) in the U.S. flight network.
  • Figure 3: Degree Distribution with Power-Law Fit: $\gamma \approx 1.89$
  • Figure 4: Communities structure with Louvain Algorithm
  • Figure 5: Communities Detected with Louvain Algorithm and Label Propagation Algorithm
  • ...and 9 more figures