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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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An algorithm based on positive and negative links for community detection in signed networks.

Yansen Su1, Bangju Wang1, Fan Cheng1

  • 1Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, 230039, China.

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This study introduces a novel random walk algorithm for community detection in signed networks. It effectively utilizes both positive and negative links to identify and merge communities, outperforming existing methods.

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community detection is crucial for understanding network structures.
  • Existing algorithms often neglect negative links, limiting their applicability to signed networks.
  • Signed networks, with both positive and negative relationships, require specialized community detection methods.

Purpose of the Study:

  • To propose a new algorithm for community detection in signed networks.
  • To leverage both positive and negative links for improved community detection accuracy.
  • To address the limitations of existing algorithms in handling signed network data.

Main Methods:

  • A novel algorithm based on random walks is developed.
  • Initial communities are identified using local maximum degree nodes.
  • Node attraction/repulsion probabilities are calculated using random walks considering link signs.
  • A community optimization method is employed for merging similar communities.

Main Results:

  • The algorithm effectively utilizes both positive and negative links.
  • Experimental results on synthetic and real-world signed networks demonstrate high performance.
  • The proposed method shows superior effectiveness compared to existing approaches.

Conclusions:

  • The developed random walk-based algorithm is effective for community detection in signed networks.
  • Incorporating both positive and negative links enhances community detection performance.
  • The algorithm provides a robust solution for analyzing complex signed network structures.