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Relational flexibility of network elements based on inconsistent community detection.

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Summary
This summary is machine-generated.

This study introduces companionship inconsistency, a new metric to understand node relationships in networks. It quantizes how often nodes are inconsistently placed in communities, revealing unique network structures and node roles.

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Area of Science:

  • Network science
  • Graph theory
  • Data analysis

Background:

  • Community identification is key to understanding network mesoscale structure.
  • Stochastic algorithms for community detection can lead to ambiguous results.
  • Existing methods often aim to resolve, not leverage, this ambiguity.

Purpose of the Study:

  • To introduce and define "companionship inconsistency" as a novel network metric.
  • To utilize community structure ambiguity to quantify node relationships.
  • To propose companionship inconsistency as a new form of node centrality.

Main Methods:

  • Defining companionship inconsistency based on node membership variability across stochastic community detection realizations.
  • Analyzing both model and real-world network data.
  • Applying the metric to social and infrastructure networks.

Main Results:

  • Companionship inconsistency reveals unique node characteristics.
  • The metric effectively identifies nodes with weak or multiple community affiliations.
  • It provides insights into network balance, such as in power grids.

Conclusions:

  • Companionship inconsistency offers a novel perspective on node centrality.
  • It quantifies a node's intrinsic relationship to higher-order network organization.
  • This metric has potential applications in diverse network types, from social to infrastructure.