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What are Populations and Communities?

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Identifying communities and key vertices by reconstructing networks from samples.

Bowen Yan1, Steve Gregory

  • 1Department of Computer Science, University of Bristol, Bristol, United Kingdom. yan@cs.bris.ac.uk

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This study introduces a novel network sampling method using random walks to uncover community structures in hidden populations. The technique effectively identifies key vertices for targeted interventions, improving network analysis and disease spread mitigation.

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Area of Science:

  • Network science
  • Epidemiology
  • Computational social science

Background:

  • Respondent-Driven Sampling (RDS) is common for studying hidden populations in epidemiology.
  • Deducing network properties from samples is crucial for understanding complex systems.
  • Discovering network structure, particularly community structure, remains a challenge.

Purpose of the Study:

  • To develop a network sampling method for discovering community structure.
  • To adapt sampling techniques for network analysis beyond traditional epidemiological uses.
  • To identify key network vertices for effective intervention strategies.

Main Methods:

  • Collecting network samples using random walks.
  • Reconstructing network topology by probabilistically coalescing vertices.
  • Utilizing vertex attributes to inform probabilistic reconstruction.

Main Results:

  • The method approximately reconstructs parts of the original network.
  • Community structure of the network is recovered with relative accuracy.
  • Key vertices crucial for reducing infection spread are identified.

Conclusions:

  • The proposed random walk-based sampling method effectively reveals network community structures.
  • This approach offers a valuable tool for analyzing hidden populations and network properties.
  • Identifying influential vertices aids in designing targeted public health interventions.