基于模块密度优化的标签传播社区发现算法

基于标签传播的社区发现算法(LPA)以其简单高效得到了广泛的研究,然而当社区结构模糊时,LPA得到的是一个单一的社区,这是无意义的.模块化标签传播算法(LPAm)则倾向于将网络划分为度数相近的社区且存在解极限问题.为此提出基于模块密度的标签传播(LPAd)算法,该算法通过对模块密度优化进行标签标记和传播,以避免过大社区的形成,且生成的社区满足Radicchi等人提出的弱社区定义.多个真实数据集和人工网络数据的实验结果表明,本文算法在不改变算法复杂度的情况下提高了所发现社区的质量,与现有的若干基于标签传播的社区发现算法相比,取得了改进的效果.

Label Propagation Community Detection Algorithm Based on Modularity Density Optimization

The simplicity and efficiency of the community detection algorithm based on label propagation (LPA) have been studied extensively, but when the community structure is not clear, a single community is obtained through the LPA, which is meaningless. Modularity-specialized label propagation algorithm (LPAm) tends to partition the network into communities with similar degrees and the problems of solving the limit of functions exist. Therefore, this paper points out label propagation algorithm based on modularity density optimization (LPAd), in order to avoid the formation of large communities, and the community meets the weak community definition proposed by Radicchi et al. Several real datasets and artificial network data experimental results show that, this algorithm raises the quality of the detected community without changing the algorithm complexity, and compared with the existing number of community detection algorithm based on label propagation, it has been improved effectively.