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A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies.

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A new Hierarchical Arc-Merging (HAM) algorithm effectively detects community structures in networks. It achieves high accuracy and scalability, addressing resolution limit issues in network analysis.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Mining

Background:

  • Community detection is crucial for understanding complex networks.
  • Existing algorithms face challenges with resolution limits and scalability.
  • Evaluating algorithms requires metrics like Normalized Mutual Information (NMI) and modularity.

Purpose of the Study:

  • To introduce and evaluate a novel Hierarchical Arc-Merging (HAM) algorithm for community detection.
  • To assess the HAM algorithm's effectiveness, examination capabilities, correctness, and scalability.
  • To demonstrate the HAM algorithm's ability to mitigate resolution limit problems.

Main Methods:

  • The study proposes a Hierarchical Arc-Merging (HAM) algorithm utilizing network topology.
  • Rule-based arc-merging strategies are introduced for community structure identification.
  • Validation employed social networks, Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and synthetic networks.

Main Results:

  • The HAM algorithm demonstrated satisfactory performance efficiency and scalability on large-scale networks.
  • HAM-identified communities showed comparability with ground-truth communities on social and LFR networks.
  • The algorithm successfully mitigated resolution limit problems in synthetic networks.

Conclusions:

  • The proposed HAM algorithm is an effective and efficient method for community detection.
  • HAM offers a robust solution for analyzing complex networks, including overcoming resolution limits.
  • The algorithm's performance validates its utility in network science and data mining applications.