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针对绝大多数社区发现算法都存在着网络节点仅隶属于一个社区的假设,引入谱图理论与粗糙集理论来分析复杂网络社区,提出一种用于网络重叠社区发现的粗糙谱聚类算法RSC,该算法用上下近似来刻画网络节点的社区归属,边界表示社区之间共享的节点,通过优化重叠社区结构模块度来实现重叠社区发现.通过3个不同类型真实网络的仿真实验,结果验证了该方法的可行性与有效性.
For the vast majority of community discovery algorithms, there are some assumptions that network nodes belong to only one community. By introducing spectrum theory and rough set theory to analyze complex network communities, a rough spectral clustering algorithm for network overlapping community discovery RSC The algorithm uses the upper and lower approximations to characterize the community ownership of network nodes, the boundaries represent the nodes shared by the communities, and the overlapped community discovery is achieved by optimizing the modularity of overlapping community structures.Through the simulation experiments of three different types of real networks, the results verify The feasibility and effectiveness of this method.