Earticle

초록

영어

According to the defects that community detection algorithm in unknown complex networks has a pre-parameter. We propose Adaptive Label Propagation Algorithm (ALPA) to detect community structures in complex networks. The ALPA algorithm find out all disjoint Maximal Clique (MC) and let each MC share the identical weight and unique label so as to reduce the redundant labels and uncontrollable factors. The stability of ALPA algorithm is enhanced by synchronous update during iterations. Meanwhile it will converge easily due to the termination condition that all of the vertexes have the label. During iterations we use the adaptive threshold method to overcome the pre-parameter limitation. Compared with other community detection algorithms in synthetic networks and real networks, our experiments show that ALPA algorithm not only improves the tolerance of mixing parameter, but also enhances its robustness.

목차

Abstract
 1. Introduction
 2. Adaptive Label Propagation Algorithm
  2.1. Initialization
  2.2. Design Alternatives
  2.3. Termination
  2.4. Post Processing
  2.5. Complexity
 3. Experiments
  3.1. Methodology
  3.2. Comparison with Other Algorithms
 4. Conclusions
  4.1. Contributions
  4.2. Future Work 
 Acknowledgments
 References

키워드

저자

• Chunying Li [ School of Computer Science, South China Normal University, Computer Network Center, Guang Dong Polytechnic Normal University, Guangzhou, China ]
• Yonghang Huang [ School of Computer Science, South China Normal University, China ]
• Zhikang Tang [ School of Computer Science, Guang Dong Polytechnic Normal University, China ]
• Yong Tang [ School of Computer Science, South China Normal University, China ] Corresponding author
• Jiandong Zhao [ Computer Network Center, Guang dong Polytechnic Normal University, China ]

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