원문정보
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
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