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A new clustering method and its application in social networks

机译:一种新的聚类方法及其在社交网络中的应用

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In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful dusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.
机译:在图论模型中,聚类是将顶点划分为组的过程,组内的边密度高于它们之间的边密度。在本文中,我们介绍了一种检测此类群体的新聚类方法,并将其用于分析一些经典的社交网络。新方法具有两个显着特征:非二元层次树和重叠聚类特征。非二叉树比大多数传统方法构造的二叉树要小得多,因此,它明显地突出了有意义的尘土,大大减少了手动选择簇的工作。通过几种基准数据集对本方法进行了测试,这些数据集的社区结构是已知的,结果表明该方法是一种从社交网络中提取社区结构的灵敏而准确的方法。

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