中文摘要：

社团分类对研究复杂网络及其特性具有重要意义,为此提出将神经网络的分类特性应用于社团分类中,以BP神经网络为模型,以Zachary研究的俱乐部成员关系网络为测试数据,利用社团节点与神经网络节点之间关系的相似性模拟社团分类结果,根据成员关系图建立邻接矩阵,利用邻接矩阵关系进行实验设计建立BP神经网络,通过已知样本学习和检验样本检验,实现成员关系网络的分类模拟,数值实验结果表明文章运用的仿真方法是可行有效的.

英文摘要：

Community classification,an important character of complex network research,can be applied from the advantage of neural network in pattern recognition.The basic idea is that the similarity relationship is introduced to simulate the classification between community and neural network by the model of back propagation neural network and the club membership network that was studied by zachary as the experimental surrounding.The method is to set up the adjacency matrix on the membership,then to build the back propagation neural network on the lab design and the adjacency matrix.The experimental outcomes are testified feasible by studying the known samples and the test samples.