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Community Detection in Semantic Networks: A Multi-View Approach.

Hailu Yang1, Qian Liu1, Jin Zhang2

  • 1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150001, China.

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Summary
This summary is machine-generated.

This study introduces a multi-view integration method for semantic social network community detection. It enhances analysis by combining diverse semantic features, outperforming traditional single-view approaches.

Keywords:
adaptive loss functioncommunity detectionmulti-view clusteringsemantic information processingsemantic social network

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Area of Science:

  • Computer Science
  • Network Analysis
  • Data Mining

Background:

  • Semantic social networks are complex systems with nodes, links, and documents.
  • Existing community detection algorithms often rely on single-view analysis, lacking granular semantic feature representation.

Purpose of the Study:

  • To propose a novel multi-view integration method for community detection in semantic social networks.
  • To effectively represent and integrate semantic features at various granularity levels.

Main Methods:

  • Developed a data feature matrix based on node similarity.
  • Extracted semantic features from word frequency, keyword, and topic views.
  • Utilized L21-norm and F-norm for an adaptive loss function to maximize mutual information.
  • Constructed an optimization expression for a unified graph matrix.

Main Results:

  • The multi-view approach significantly improves semantic information analysis compared to single-view methods.
  • The proposed method demonstrates superior performance in community detection over traditional algorithms and multi-view clustering.

Conclusions:

  • Multi-view integration is crucial for effective community detection in semantic social networks.
  • The developed method offers a robust and high-performing solution for uncovering community structures.