Earticle

초록

영어

A new algorithm named Differential Evolution Algorithm for Community Detection (DEACD) was proposed in the paper. DEACD used DE as its search engine and used the network modularity as the fitness function to search for an optimal community partition of a network. In this algorithm, there is a modified binomial crossover mechanism to transmit some important information about the community structure in evolution effectively. In addition, a biased process and clean-up operation were employed in DEACD to improve the quality of the community partitions detected in evolution. Experimental results showed that DEACD has very competitive performance compared with other state-of-the-art community detection algorithms. In the process of evolution, the colony evolution was conducted under DE scheme, the network modularity was used to evaluate the fitness of individuals in the colony. The performance of DECD was analyzed by computer generated network and real-world network examples. The algorithm was implemented using matlab Genetic Algorithm Optimization Toolbox (GAOT), and the parametric analysis was performed in the experiment.

목차

Abstract
 1. Introduction
 2. State of the Art of the Community Detection in Complex Network
 3. The Detection Algorithm for Complex Network based on DEA
  3.1. Individual Expression
  3.2. Fittness Function
 4.The Experimental Evaluation
  4.1. Computer Generated Network
  4.2. Networks in real-world
 5. Conclusion
 References

키워드

저자

• Guoshun Wang [ Central South University, Changsha ]
• Xuan Zhang [ Central South University, Changsha ]
• Guanbo Jia [ Central South University, Changsha ]
• Xiaoping Ren [ National institute of metrology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605