Tracking the Evolution of Infrastructure Systems and Mass Responses Using Publically Available Data

Area of Science:

• Network Science
• Complex Systems
• Data Science

Background:

• Network evolution is a key concept in network science but understudied in empirical research.
• Real-world networks, like infrastructure and social networks, are often perceived as static in the short term.
• Limited data availability hinders the study of short-term dynamics in evolving networks.

Purpose of the Study:

• To demonstrate that both infrastructure and social networks exhibit evolutionary dynamics at the system level in the short term.
• To develop and apply a methodology for tracking network evolutionary dynamics.
• To analyze network changes during Hurricane Sandy using real-world data.

Main Methods:

• Developed a methodology incorporating network flows and microstructure (motifs) to track evolutionary dynamics.
• Applied the methodology to human interaction, subway, and taxi networks.
• Utilized public Twitter and transportation data during Hurricane Sandy.

Main Results:

• Confirmed that infrastructure and social networks show system-level evolutionary dynamics in the short term.
• Observed shifting between different network phases, consistent with network science predictions.
• Detected significant, continuous changes in the system-level structure of the studied networks.

Conclusions:

• Real-world networks demonstrate significant short-term evolutionary dynamics.
• The developed methodology enables continuous tracking of network structural changes.
• This approach offers a promising avenue for real-time network monitoring and analysis.