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Area of Science:

  • Network Science
  • Probability Theory
  • Statistical Inference

Background:

  • Random graph models are crucial for understanding complex networks.
  • Dynamic network analysis requires methods to infer changing structures.
  • The Chung-Lu model provides a framework for degree-based random graphs.

Purpose of the Study:

  • To introduce and analyze a dynamic version of the Chung-Lu random graph model.
  • To develop and validate a statistical inference technique for estimating network dynamics from partial observations.
  • To assess the performance of the proposed inference method using numerical experiments.

Main Methods:

  • Modeling edges as alternating between present and absent states over time.
  • Developing an inference technique to estimate dynamic parameters from edge count snapshots.
  • Conducting numerical simulations to test the accuracy and efficacy of the inference method.

Main Results:

  • The proposed inference technique can effectively estimate the underlying dynamics of the dynamic Chung-Lu graph.
  • The method's performance is robust across various simulation scenarios.
  • Partial information, specifically edge counts, is sufficient for accurate dynamic inference.

Conclusions:

  • Dynamic random graph models offer a richer representation of evolving networks.
  • The developed inference technique provides a practical tool for analyzing time-varying network structures.
  • This work contributes to the field of statistical network analysis and dynamic systems modeling.