基于模块度优化的标签传播社区发现算法

标签传播算法（LPA）是一种快速高效的社区发现算法，算法无需社区数量等先验信息，但存在大量随机性，稳定性较差. 为了提高标签传播算法的稳定性，提出了一种改进的标签传播算法（LPAMP）. 该算法分为两个阶段，第一阶段以模块度贪婪为依据，进行节点粗聚类；第二阶段在粗聚类的基础上，进行节点标签传播. 实验结果表明，所提算法降低了标签传播算法的随机性，增强了稳定性，并且提高了准确率.

Community Detection for Label Propagation with Modularity Optimization

Label Propagation Algorithm (LPA) is a fast and efficient community detection algorithm and this algorithm does not need to know the prior information such as the number of communities. However, this algorithm has a large number of randomness, which leads to unstable results. In order to improve the stability of label propagation algorithm, we propose an improved label propagation algorithm (LPAMP). The algorithm is divided into two phases. In the first phase, vertices are clustered roughly by optimizing the modularity greedily; in the second phase, the labels propagate through the network based on the result of the first phase. Experimental results show that the proposed algorithm not only reduces the randomness of the label propagation algorithm, but also improves the stability and increases the accuracy.