Earticle

초록

영어

This paper proposes the community detection in complex networks based on improved genetic algorithm and local optimization (IGALO) in terms of the defect that traditional community detection approaches based on genetic algorithm have strong randomness and weak searching ability in the process of community detection. Taking modularity function Q as the objective function, IGALO algorithm adopts label propagation method of one-iteration to initialize population so as to generate initial population with certain precision. Then, anti-destructive one-way crossover strategy is proposed to ensure the crossover operation to develop in the direction of making community structure increase to modularity function. Finally, mutation strategy of node local optimization is proposed to improve the searching efficiency of algorithm. This algorithm effectively overcomes the defect that traditional algorithms have weak searching ability and improves the community detection accuracy. Tests are made on benchmark networks and real-world networks and comparative analysis is also made with various classic algorithms. The results show that IGALO algorithm is effective and feasible.

목차

Abstract
 1. Introduction
 2. IGALO Algorithm
  2.1. Objective Functions
  2.2. Encoding Method
  2.3. Initial Population
  2.4. Crossover Strategy
  2.5. Mutation Strategy
  2.6. Description of IGALO Algorithm
  2.7. Analysis of Algorithm Time Complexity
 3. Experiments
  3.1. Evaluation Criteria
  3.2. Dataset Experiment of Benchmark Networks
  3.3. Dataset of Real-World Networks
 4. Conclusion
 References

키워드

저자

• Kun Deng [ College of Mathematics Physics and Information Engineering, JiaXing University, JiaXing, China ]
• XingYan Liu [ College of Mathematics Physics and Information Engineering, JiaXing University, JiaXing, China ]
• WenPing Li [ College of Mathematics Physics and Information Engineering, JiaXing University, JiaXing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605