Chaotic memetic algorithm and its application for detecting community structure in complex networks

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.