一种可变分辨率的社团发现算法  

A Community Discovery Algorithm with Variable Resolution

作　　者：陈东明[1] 夏方朝[1] 贾路路[1] 徐晓伟[2] 

机构地区：[1]东北大学软件学院,辽宁沈阳110819 [2]阿肯色大学信息科学系,美国小石城72204

出　　处：《东北大学学报（自然科学版）》2012年第3期348-351,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(60872040);辽宁省自然科学基金资助项目(20102059)

摘　　要：介绍了复杂网络及社团结构的相关概念,给出节点的综合特征值和增益函数的定义,然后提出一种新的社团发现算法(CNCD).综合特征值与节点的度数及其聚类系数有关,用于发现社团中的核心节点;增益函数决定何时获得社团结构的最佳划分.作者用C++语言实现算法,并使用经典数据集对算法进行验证,实验结果表明此算法不仅能够得到正确的社团结构,而且通过动态调整算法中的参数值,能够得到比传统算法更加详细的社团划分结果,获得网络的细节信息.Introducing some relational conceptions of complex network and community structure,a new community discovery algorithm based on core nodes detecting was proposed.Integrated feature value and gain function are employed in the new algorithm.Comprehensive eigenvalue which is related to the node degree and clustering coefficient is used to detect core nodes in community and the gain function judges when to get the best partition of community structure.The new algorithm is implemented with C＋＋.Experimental results on the classic datasets demonstrate the feasibility and effectiveness of the algorithm.Further,the parameter can be tuned to get more detailed structure of the complex network than traditional algorithm which is very useful in many situations.

关 键 词：复杂网络 社团结构 核心节点 综合特征值 增益函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]