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Overlapping Community Detection Based on Attribute Augmented Graph.

Hanyang Lin1,2, Yongzhao Zhan1,2, Zizheng Zhao3

  • 1School of Computer Science and Communications Engineering, Jiangsu University, Zhenjiang 212013, China.

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Summary
This summary is machine-generated.

This study introduces a new algorithm for detecting overlapping communities in social networks using attribute information. The enhanced method accurately identifies communities and automatically determines their number, outperforming existing approaches.

Keywords:
attributed networksaugmented attribute graphcommunity detection

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Area of Science:

  • Computer Science
  • Data Science
  • Network Analysis

Background:

  • Real-world social networks contain rich attribute information beyond topology.
  • Overlapping vertices belonging to multiple communities pose challenges for traditional community detection.
  • Existing methods struggle to fully leverage attribute data for accurate overlapping community detection.

Purpose of the Study:

  • To propose an overlapping community detection algorithm utilizing augmented attribute graphs.
  • To improve accuracy by incorporating an improved attribute weight adjustment strategy.
  • To enhance the algorithm for automatic determination of the number of communities.

Main Methods:

  • Developed an overlapping community detection algorithm based on an augmented attribute graph.
  • Integrated an improved weight adjustment strategy for attribute data.
  • Implemented a node-density-based fuzzy k-medoids process for automatic community number determination.

Main Results:

  • The proposed algorithm effectively detects overlapping communities in both synthetic and real-world datasets.
  • The enhanced method demonstrates improved accuracy in identifying overlapping communities.
  • The algorithm requires fewer parameters compared to baseline methods.

Conclusions:

  • The augmented attribute graph approach is effective for overlapping community detection.
  • Automatic determination of community number enhances algorithm usability.
  • The proposed method offers a more accurate and parameter-efficient solution for analyzing complex social networks.