本文充分利用社会网络中存在普适幂律分布的特性，提出了基于核心节点的局部社区发现算法（EALCN），利 用改进的PageRank 进行节点排序，然后利用网络中的局部信息对局部目标函数进行优化，从初始的种子节点不断优化后获得 目标函数，最终获取局部社区，仿真实验表明，该算法利用少量的局部信息便能够比较快速的找出社区结构，具有较高的执行 效率。

This paper takes advantage of the universal power-law distribution of social networks and proposes a leader based local community detection algorithm EALCN. It uses local structural information in the network to optimize a local objective function. A local community can be detected through continuous optimization of the function by expanding from an initial core member computed by a modified PageRank sorting algorithm. The advantage of this algorithm is that it only uses some local information of the network to detect communities by utilizing highly important nodes. The efficiency is higher than traditional algorithm