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This study introduces Lower Ricci Curvature (LRC), a novel network analysis method. LRC enhances community detection in large networks with linear complexity, improving existing algorithms.

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Area of Science:

  • Network Science
  • Graph Theory
  • Data Mining

Background:

  • Existing curvature-based methods for network analysis face computational challenges.
  • There is a need for scalable and efficient discrete curvature measures for large networks.

Purpose of the Study:

  • Introduce Lower Ricci Curvature (LRC) as a novel, scalable, and scale-free discrete curvature.
  • Develop an LRC-based preprocessing method to improve community detection algorithms.

Main Methods:

  • Introduced Lower Ricci Curvature (LRC), a discrete curvature measure.
  • Developed a preprocessing technique utilizing LRC.
  • Applied the method to diverse real-world networks (e.g., NCAA, DBLP, Amazon, YouTube).

Main Results:

  • LRC offers linear computational complexity, suitable for large-scale network analysis.
  • The LRC-based preprocessing method significantly enhances the performance of various community detection algorithms.
  • Demonstrated efficacy across multiple real-world datasets.

Conclusions:

  • Lower Ricci Curvature (LRC) is an effective and efficient tool for network analysis.
  • LRC-based preprocessing improves community detection in complex networks.
  • The method shows broad applicability in analyzing large-scale network structures.