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Area of Science:

  • Network science
  • Computational epidemiology
  • Data analysis

Background:

  • Temporal networks are often studied using discrete time snapshots.
  • Aggregating these snapshots can simplify analysis but may alter dynamics.
  • Efficient methods are needed to compress temporal network data without losing critical information.

Purpose of the Study:

  • To develop a method for compressing temporal network chronologies.
  • To assess the impact of snapshot aggregation on network dynamics.
  • To validate the method using epidemic modeling on real-world data.

Main Methods:

  • Proposing a novel method to progressively combine snapshot pairs.
  • Utilizing matrix commutators to quantify the dynamical effect of aggregation.
  • Applying the compression technique to epidemic modeling simulations.

Main Results:

  • The proposed method allows for significant compression of temporal network data.
  • Snapshot aggregation, when guided by minimal dynamical effect, preserves epidemic dynamics.
  • The approach was successfully demonstrated on contact tracing datasets.

Conclusions:

  • Temporal network analysis can benefit from intelligent snapshot aggregation.
  • The proposed method offers a balance between data compression and dynamical fidelity.
  • This technique is valuable for computational epidemiology and large-scale network studies.