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A community partitioning algorithm for cyberspace.

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  • 1Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450001, Henan, China.

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This study introduces the PR-LFM algorithm for improved cyberspace mapping. It effectively partitions online communities by considering hierarchy and information flow, outperforming traditional methods.

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Area of Science:

  • Computer Science
  • Network Science
  • Cybersecurity

Background:

  • Community partitioning is vital for cyberspace mapping.
  • Existing algorithms neglect hierarchy, overlap, and information directionality.
  • Traditional methods are inadequate for effective cyberspace resource division.

Purpose of the Study:

  • To develop a novel algorithm for cyberspace community partitioning.
  • To address limitations of existing methods by incorporating network hierarchy and information flow.

Main Methods:

  • Introduced the PageRank-Local Fitness Maximization (PR-LFM) algorithm.
  • Combined improved Local Fitness Maximization (LFM) with PageRank (PR).
  • Determined seed nodes via degree centrality and employed local community expansion.

Main Results:

  • The PR-LFM algorithm demonstrated effective partitioning of cyberspace resources.
  • Experimental data validated the algorithm's superior performance in resource division.
  • The method successfully retained community structure by prioritizing node importance.

Conclusions:

  • The PR-LFM algorithm offers a more effective approach to cyberspace community partitioning.
  • Incorporating network hierarchy and information directionality enhances community division accuracy.
  • This method provides a robust solution for mapping and understanding cyberspace.