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Detecting community structure in signed and unsigned social networks by using weighted label propagation.

Chaos (Woodbury, N.Y.)·2020
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Chaotic memetic algorithm and its application for detecting community structure in complex networks.

Bagher Zarei1, Mohammad Reza Meybodi2, Behrooz Masoumi1

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This study introduces a novel Chaotic Memetic Algorithm for detecting community structure in complex networks. The algorithm enhances efficiency and prevents local optima, outperforming existing methods on benchmark datasets.

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Area of Science:

  • Network Science
  • Computational Complexity
  • Data Mining

Background:

  • Community structure is a key topological feature of complex networks, crucial for understanding network function and organization.
  • Detecting community structure is a challenging analytical problem with significant implications.
  • Modularity maximization is a common approach, but existing algorithms face limitations.

Purpose of the Study:

  • To propose a novel Chaotic Memetic Algorithm (CMA) for accurate and efficient community structure detection in complex networks.
  • To improve upon existing algorithms by enhancing convergence speed and preventing local optima.
  • To validate the effectiveness of the proposed CMA against state-of-the-art methods.

Main Methods:

  • A hybrid approach combining a genetic algorithm for global search and a specialized local search.
  • Utilization of chaotic numbers instead of random numbers in both global and local search processes to enhance diversity and convergence.
  • Testing the algorithm on both synthetic and real-world benchmark networks.

Main Results:

  • The Chaotic Memetic Algorithm demonstrated superior performance in detecting community structures.
  • The use of chaotic numbers effectively preserved population diversity and avoided local optima.
  • Experimental results showed the proposed algorithm is competitive with current state-of-the-art methods.

Conclusions:

  • The Chaotic Memetic Algorithm is an effective and efficient method for community structure detection in complex networks.
  • The integration of chaotic dynamics offers a significant advantage in overcoming limitations of traditional search algorithms.
  • This approach provides a valuable tool for analyzing the organization and function of complex systems.