中文摘要：

挖掘复杂网络的社团结构对研究复杂系统具有重要的理论和实践意义．其中，相较于全局社团，局部社团的挖掘难度更大，相关文献更少．现有的局部社团挖掘算法大都精度较低、稳定性较差．本文提出了一个有效的局部社团挖掘算法，称为内外夹推法（Shell interception and core expansion，SICE）．算法有两个创新之处：1）将节点相似度模型引入到局部社团挖掘算法中（节点相似度模型在局部社团挖掘中较难应用），并提出了“一次一个子图”的社团扩展模式；2）提出了一种“内外夹推”的思想．这两个创新使SICE算法摆脱了缺乏网络全局信息的困扰，并解决了以往算法的一个致命缺陷，从而使算法具有很高的精度和稳定性．通过理论分析和实验比较，证明SICE算法要远好于当前的同类算法，甚至不逊色于性能较好的全局社团挖掘算法．

英文摘要：

Community structure detection bears both theoretical and practical significance for the study of complex sys- tems. Generally speaking, the local community detection is relatively a more difficult problem than the global community detection. So up to now, the related researches are still slow progress. And there are many defects existing in the previous local community detection algorithms, such as low precision and poor stability. In this paper, a local community detection algorithm, which is called shell interception and core expansion （SICE）, has been proposed. There are two innovations in this algorithm： 1） A node similarity model is introduced into this algorithm, and a community expansion mode ＂one sub- graph at a time＂ is proposed; 2） An effective method which is named ＂shell interception and core expansion＂ is proposed. By these two innovations, the SICE algorithm has solved the problem of missing global network information, and avoided a fatal weakness of the previous algorithms. Theoretical analysis and experiments all illustrate that the SICE algorithm has high precision and stability, and it outperforms the previous algorithms.