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Identifying Influencers in Social Networks.

Xinyu Huang1, Dongming Chen1, Dongqi Wang1

  • 1Software College, Northeastern University, Shenyang 110169, China.

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|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for identifying key influencers in complex social networks by analyzing local connections. The approach effectively handles large datasets and diverse interactions, outperforming existing methods.

Keywords:
complex networkmultilayer networknode influencesocial network analysis

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Area of Science:

  • Social network analysis
  • Informatics
  • Sociology
  • Mathematics

Background:

  • Online social media has become a primary platform for knowledge and interest sharing.
  • Identifying influential individuals across diverse social networks presents a significant challenge due to complex interactions and large data volumes.

Purpose of the Study:

  • To propose a novel method for identifying influencers in multilayer social networks.
  • To address the challenges posed by complex interactions and large data scales in social network analysis.

Main Methods:

  • A general multilayer network model is utilized to represent multiple interconnected social networks.
  • A node influence indicator is developed based solely on local neighboring information.

Main Results:

  • The proposed method demonstrates superior performance compared to existing approaches.
  • Extensive experiments on 21 real-world datasets validate the effectiveness of the new indicator.

Conclusions:

  • The developed node influence indicator offers a robust solution for identifying influencers in complex social networks.
  • This research contributes to understanding social network evolution and provides a foundation for future studies in the field.