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Community Detection Method Based on Two-layer Dissimilarity of Central Node

机译：基于中心节点两层差异的社区检测方法

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Studying community discovery algorithms for complex networks is necessary to determine the origin of opinions, analyze the mechanisms of public opinion transmission, and control the evolution of public opinion. The problem of the existing clustering algorithm of the central node having a low quality of community detection must also be solved. This study proposes a community detection method based on the two-layer dissimilarity of the central node (TDCN-CD). First, the algorithm selects the central node through the degree and distance of the node. Selecting nodes in the same community as the central node at the same time is avoided. Simultaneously, the algorithm proposes the dissimilarity index of nodes based on two layers, which can deeply explore the heterogeneity of nodes and achieve the effect of accurate community division. The results of using Karate and Dolphins datasets for simulation show that compared to the Girvan-Newman and Fast-Newman classical community partitioning algorithms, the TDCN-CD algorithm can effectively detect the community structure and more accurately divide the community.
机译：研究复杂网络的社区发现算法对于确定舆论的起源，分析舆论传播的机制以及控制舆论的发展是必要的。还必须解决现有的中心节点聚类算法的社区检测质量低的问题。这项研究提出了一种基于中心节点两层不相似性的社区检测方法（TDCN-CD）。首先，该算法通过节点的程度和距离选择中心节点。避免同时选择与中央节点位于同一社区的节点。同时，该算法在两层基础上提出了节点的相似度指标，可以深入探索节点的异质性，达到准确的社区划分的效果。使用空手道和海豚数据集进行仿真的结果表明，与Girvan-Newman和Fast-Newman经典社区划分算法相比，TDCN-CD算法可以有效地检测社区结构并更准确地划分社区。

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