Community detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and gives the detailed steps of the algorithm and simulation results. We compare the proposed algorithm with a series of typical algorithms through experiments on synthetic and actual networks. Experimental results on artificial and real networks demonstrate the effectiveness and superiority of our algorithm.

中文翻译：

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复杂网络中节点介数和相似性的社区检测

复杂网络的社区检测一直是一个热点问题。随着混合参数μ 的增加，网络复杂度增加，社区检测算法需要改进。本文在前人工作的基础上，从节点介数特性的角度设计了一种新颖的算法，并给出了算法的详细步骤和仿真结果。我们通过在合成网络和实际网络上的实验将所提出的算法与一系列典型算法进行了比较。在人工和真实网络上的实验结果证明了我们算法的有效性和优越性。