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Efficient limited-time reachability estimation in temporal networks.

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We developed a faster method to track reachability in temporal networks with limited waiting times. This approach efficiently analyzes spreading processes on large networks, improving disease outbreak predictions.

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Area of Science:

  • Network Science
  • Computational Science
  • Dynamical Systems

Background:

  • Many real-world processes on networks have time constraints, such as disease recovery or information spread.
  • These time-limited dynamics are crucial in temporal network models.
  • Current simulation methods for analyzing these processes are computationally intensive.

Purpose of the Study:

  • To propose a significantly more efficient method for tracking reachability in temporal networks with limited waiting times.
  • To enable simultaneous estimation of reachability from all network nodes and times.
  • To facilitate analysis of large-scale temporal network dynamics.

Main Methods:

  • Developed a novel algorithm for computing temporal path reachability.
  • The method provides simultaneous in- or out-reachability estimates for all nodes and times.
  • Algorithm designed for efficiency on large temporal networks with millions of events.

Main Results:

  • Achieved orders-of-magnitude improvement in efficiency compared to traditional simulation methods.
  • The method successfully handles very large temporal networks on commodity hardware.
  • Demonstrated the capability to compute global reachability-based centralities and identify optimal sources for spreading processes.

Conclusions:

  • The proposed method offers a scalable and efficient solution for analyzing time-limited processes on temporal networks.
  • Enables new avenues for studying network dynamics, including epidemic modeling and centrality analysis.
  • Facilitates the analysis of complex spreading phenomena in large-scale temporal systems.