一种基于结构信息的改进CNM算法

An improved CNM algorithm based on network structure information

CNM(clauset-newman-moore)算法能有效划分网络社区结构,但是对应划分出的社区准确度不高。对此,结合网络结构信息提出了一种改进CNM算法。通过对输入数据进行迭代删边预处理,精简网络结构,将原始网络分为两个子网络,然后将CNM算法应用到子网络,完成社区发现。在五个不同规模数据集上的试验结果表明,改进CNM方法提高了社区发现的质量和精度,社区模块度在小规模的数据集上得到了显著提升。

Although community detection could be effectively accomplished by CNM（ clauset-newman-moore） algorithm,the accuracy of the results was unsatisfactory. Consequently,an improved CNMalgorithm based on network structure information was proposed,which divided the original network into two parts by removing the edge whose edge betweenness was maximum of all iteratively. These two parts as the input data of CNMalgorithm were used to detect communities. The experimental results on five different size of datasets showed that the improved CNMalgorithm elevated the quality of community detection,and modularity of these communities peformed well especially in small datasets.