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Identifying node role in social network based on multiple indicators.

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Summary
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This study proposes a novel method for identifying important nodes in social networks by using strongly correlated indicators like degree, ego-betweenness centrality, and eigenvector centrality. The approach effectively evaluates node importance and identifies core or bridge roles.

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Area of Science:

  • Social Network Analysis
  • Graph Theory
  • Network Science

Background:

  • Evaluating node importance and identifying core/bridge roles are fundamental in social network analysis.
  • Single indicators are insufficient for comprehensive node characteristic analysis, necessitating the use of multiple indicators.
  • Selecting indicators with weak correlations is often intuitive but less effective than using strongly correlated ones.

Purpose of the Study:

  • To develop and validate a method for evaluating node importance and identifying core/bridge roles in networks.
  • To demonstrate the superiority of using strongly correlated indicators over weakly correlated ones for node analysis.
  • To propose a new metric for node importance based on a normalized sum of selected centrality measures.

Main Methods:

  • Utilized degree, ego-betweenness centrality, and eigenvector centrality as key indicators.
  • Defined node importance as the normalized sum of these three indicators.
  • Identified core candidates by high degree and bridge candidates by high ego-betweenness centrality.
  • Determined node roles by analyzing deviations from statistical indicator correlations within the network.

Main Results:

  • The proposed method effectively evaluates node importance across diverse networks.
  • The approach successfully identifies nodes acting as network cores or bridges.
  • Experiments on 18 real-world and 3 model networks validate the method's performance.

Conclusions:

  • Selecting strongly correlated indicators enhances the accuracy of node importance evaluation and role identification.
  • The proposed method offers a robust framework for analyzing node significance in complex networks.
  • This approach provides valuable insights for understanding network structures and dynamics.