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Random walk based snapshot clustering for detecting community dynamics in temporal networks.

Filip Blašković1, Tim O F Conrad2, Stefan Klus3

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This study introduces a new random walk method to analyze temporal networks. It identifies stable community structures in time-series data, revealing significant network evolution like community merging or splitting.

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Area of Science:

  • Complex Systems Analysis
  • Network Science
  • Data Mining

Background:

  • Dynamical systems evolution is often modeled using temporal networks, represented as sequences of static snapshots.
  • Understanding community structure stability and shifts in these networks is crucial for analyzing complex system dynamics.

Purpose of the Study:

  • To introduce a novel random walk-based approach for identifying stable community structures in temporal network snapshots.
  • To enable the detection of significant structural shifts, such as community splitting, merging, births, and deaths.
  • To provide a low-dimensional representation of network snapshots for comparative analysis.

Main Methods:

  • A novel random walk-based algorithm is proposed to cluster time-snapshots based on community structure stability.
  • An agent-based algorithm is developed for generating synthetic temporal network datasets for validation.
  • The approach is tested on social dynamics models and real-world datasets, comparing performance against state-of-the-art methods.

Main Results:

  • The random walk approach effectively identifies clusters of time-snapshots with stable community structures.
  • The method successfully detects significant temporal shifts in network communities.
  • A low-dimensional embedding places snapshots with similar community structures adjacently in feature space.

Conclusions:

  • The developed random walk-based technique accurately captures and analyzes the dynamics of complex systems represented by temporal networks.
  • This approach offers a robust method for detecting and understanding structural changes in evolving networks.
  • The technique demonstrates broad applicability across various dynamic network analysis tasks.