Liu Chuang, Du Yingkui, Lei Jiahao
School of Information Engineering, Shenyang University, Liaoning 110044, China.
Entropy (Basel). 2019 May 25;21(5):533. doi: 10.3390/e21050533.
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms.
现实世界充满了丰富且有价值的复杂网络。社区结构是复杂网络的一个重要特征，它使得发现某些结构或隐藏的相关信息成为可能，以便深入研究复杂网络的结构和功能特性。针对复杂网络中的社区检测问题，本文提出了一种基于自组织映射（SOM）网络的膜算法。首先，通过选择优化函数将社区检测转化为离散优化问题。其次，设计了膜算法的三个要素，即对象、反应规则和膜结构，以分析社区结构的性质和特征。第三，采用自组织映射通过在决策空间中学习和挖掘当前对象的结构来确定膜的数量，这有利于通过构建邻域关系来指导所提算法的局部和全局搜索。最后，在合成基准网络和四个真实世界网络上进行了仿真实验。实验证明，与其他实验算法的结果相比，所提算法具有更高的准确性、稳定性和执行效率。
