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Community Detection in Social Networks Using Affinity Propagation with Adaptive Similarity Matrix.

Sona Taheri1, Asgarali Bouyer1

  • 1Department of Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

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|May 14, 2020
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Summary
This summary is machine-generated.

This study introduces Affinity Propagation with Adaptive Similarity (APAS) for network community detection. APAS improves accuracy by using an adaptive, asymmetric similarity matrix, outperforming the standard Affinity Propagation method.

Keywords:
adaptive similarity matrixaffinity propagationcommunity detectiondata clusteringsocial networks

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Area of Science:

  • Network Science
  • Data Mining
  • Machine Learning

Background:

  • Community detection is crucial for understanding network structures.
  • Existing methods like Affinity Propagation (AP) offer advantages but can be improved.
  • AP requires no predefined number of communities, making it flexible.

Purpose of the Study:

  • To propose a novel Affinity Propagation with Adaptive Similarity (APAS) method.
  • To enhance community detection accuracy by adapting similarity measures.
  • To address the limitation of symmetric similarity in the standard AP algorithm.

Main Methods:

  • Developed APAS, a new version of Affinity Propagation.
  • Introduced an adaptive similarity matrix to transform symmetric similarities into asymmetric ones.
  • Evaluated APAS on artificial and real-world network datasets.

Main Results:

  • APAS demonstrated superior accuracy compared to the standard AP method.
  • The adaptive similarity matrix effectively captures leadership probabilities between nodes.
  • Experimental results validate the effectiveness of the proposed APAS approach.

Conclusions:

  • APAS provides a more accurate and effective approach for community detection.
  • The adaptive and asymmetric similarity measure is key to APAS's improved performance.
  • This method offers a valuable advancement in network analysis and clustering.