Community detection is a fundamental problem in the analysis of complex networks. It is the analogue of clustering in network data mining. Within community detection methods, hierarchical algorithms are popular. However, their iterative nature and the need to recompute the structural properties used to split the network (i.e. edge betweenness in Girvan and Newman's algorithm), make them unsuitable for large network data sets. In this paper, we study how local structural network properties can be used as proxies to improve the efficiency of hierarchical community detection while, at the same time, achieving competitive results in terms of modularity. In particular, we study the potential use of the structural properties commonly used to perform local link prediction, a supervised learning problem where community structure is relevant, as nodes are prone to establish new links with other nodes within their communities. In addition, we check the performance impact of network pruning heuristics as an ancillary tactic to make hierarchical community detection more efficient

中文翻译：

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使用局部结构特性提高分层社区检测方法的效率

社区检测是复杂网络分析中的一个基本问题。它类似于网络数据挖掘中的聚类。在社区检测方法中，分层算法很流行。然而，它们的迭代性质和重新计算用于分割网络的结构属性的需要（即 Girvan 和 Newman 算法中的边缘介数）使它们不适合大型网络数据集。在本文中，我们研究了如何将局部结构网络属性用作代理以提高分层社区检测的效率，同时在模块化方面取得有竞争力的结果。特别是，我们研究了常用于执行局部链接预测的结构特性的潜在用途，这是一个与社区结构相关的监督学习问题，因为节点倾向于与其社区内的其他节点建立新的联系。此外，我们检查了网络修剪启发式的性能影响作为辅助策略，以使分层社区检测更有效