The idea of the modal formulation of density-based clustering is to associate groups with the regions around the modes of the probability density function underlying the data. The correspondence between clusters and dense regions in the sample space is here exploited to discuss an extension of this approach to the analysis of social networks. Conceptually, the notion of high-density cluster fits well the one of community in a network, regarded to as a collection of individuals with dense local ties in its neighbourhood. The lack of a probabilistic notion of density in networks is turned into a strength of the proposed method, where node-wise measures that quantify the role of actors are used to derive different community configurations. The approach allows for the identification of a hierarchical structure of clusters, which may catch different degrees of resolution of the clustering structure. This feature well fits the nature of social networks, disentangling different involvements of individuals in aggregations.

中文翻译：

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基于密度的社交网络聚类

基于密度的聚类的模态公式的想法是将组与围绕数据的概率密度函数的模态周围的区域相关联。这里利用样本空间中集群和密集区域之间的对应关系来讨论这种方法在社交网络分析中的扩展。从概念上讲，高密度集群的概念非常适合网络中的社区，被认为是在其附近具有密集本地联系的个体的集合。网络中密度的概率概念的缺乏变成了所提出方法的优势，其中量化参与者角色的节点度量用于推导不同的社区配置。该方法允许识别集群的层次结构，这可能会捕获聚类结构的不同程度的分辨率。此功能非常适合社交网络的性质，将个人在聚合中的不同参与解开。