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Sampling networks by nodal attributes.

Yohsuke Murase1, Hang-Hyun Jo2,3,4, János Török5,6,7

  • 1RIKEN Center for Computational Science, 7-1-26, Minatojima-minami-machi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.

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Summary
This summary is machine-generated.

Node behavior in social networks can cause sampling bias in multiplex networks. Our study provides a framework to analyze this bias, revealing how sampled networks may exhibit properties not present in the original, highlighting risks in generalizing findings.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Statistical Physics

Background:

  • Social networks are often analyzed using sampled data, which can introduce bias.
  • Multiplex networks, with multiple communication channels, present unique sampling challenges.
  • Node behavior in selecting communication channels influences network sampling.

Purpose of the Study:

  • To develop a general framework for understanding network sampling models where link probability depends on node attributes.
  • To derive analytic expressions for sampled network characteristics under arbitrary nodal attribute distributions and sampling probabilities.
  • To investigate the impact of correlated nodal attributes on sampling bias.

Main Methods:

  • Developed a general mathematical setting for network sampling models.
  • Derived exact analytic expressions for degree distribution, degree correlation, and clustering spectrum of sampled networks.
  • Analyzed the influence of nodal attributes and their correlations on sampling bias.
  • Validated analytic results with numerical simulations.

Main Results:

  • Sampled network properties are shown to be weighted sums of original network topology features.
  • Sampling-induced properties can emerge in the sampled network, absent in the original.
  • The formalism was generalized to account for correlated nodal attributes, showing good agreement with simulations.

Conclusions:

  • Node behavior-driven sampling bias in multiplex networks can significantly alter network properties.
  • Naive generalization of sampled network findings to the entire network carries inherent risks.
  • The developed framework offers a robust method for analyzing and understanding attribute-based sampling bias in networks.