| 摘 要: 复杂网络的节点聚集呈现符合社区结构的动态、无标度和非对称的特性，为了优化复杂网络的社区结 构，研究当前发现和优化社区结构的方法的不足，研究用约束正态分布来改进社区结构的节点聚集归属方法，借助信息 熵，提出了基于正太分布的复杂网络结构划分算法，通过算法得出聚集节点的正态分布概率，用正太分布概率作为信息 熵的输入，重新调整信息熵的变化，根据信息熵变化的幅度，确定节点的划分归属。本算法在确定网络社区结构划分的 同时，也能够确定社区内节点的模糊关系。 |
| 关键词: 正太分布；复杂网络；社区结构；结构精简；优化算法 |
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中图分类号: TP393
文献标识码: A
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| 基金项目: 2019年度广西高校中青年教师科研基础能力提升项目《“线上”课程资源多维智能体网络的一致性安全控制问题研究》（项目编号:2019KY1512）. |
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| A Complex Network Structural Division Algorithm Based on Normal Distribution |
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DUAN Zhongxiang
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( Guangxi Vocational College Technology and Business, Nanning 530007, China)
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| Abstract: The node aggregation of complex networks presents the dynamic,fuzzy and asymmetrical characteristics of the community structure.In order to optimize the community structure of complex networks,the paper studies the current methods of discovering and optimizing community structures,and the node aggregation method to improve the community structure by constraining normal distribution.Based on information entropy,the paper proposes a complex network structure partitioning algorithm based on positive distribution.The normal distribution probability of the aggregation node is obtained by the algorithm.The positive distribution probability is used as the input of information entropy to re-adjust the information entropy.According to the magnitude of the information entropy change,the division of the node is determined.The algorithm can determine the fuzzy relationship of nodes in the community while determining the division of the network community structure. |
| Keywords: normal distribution;complex network;community structure;structure simplification;optimization algorithm |
