Palowitch John, Bhamidi Shankar, Nobel Andrew B
Department of Statistics and Operations Research University of North Carolina at Chapel Hill Chapel Hill, NC 27599.
J Mach Learn Res. 2018 Apr;18.
Community detection is the process of grouping strongly connected nodes in a network. Many community detection methods for -weighted networks have a theoretical basis in a null model. Communities discovered by these methods therefore have interpretations in terms of statistical significance. In this paper, we introduce a null for weighted networks called the continuous configuration model. First, we propose a community extraction algorithm for weighted networks which incorporates iterative hypothesis testing under the null. We prove a central limit theorem for edge-weight sums and asymptotic consistency of the algorithm under a weighted stochastic block model. We then incorporate the algorithm in a community detection method called CCME. To benchmark the method, we provide a simulation framework involving the null to plant "background" nodes in weighted networks with communities. We show that the empirical performance of CCME on these simulations is competitive with existing methods, particularly when overlapping communities and background nodes are present. To further validate the method, we present two real-world networks with potential background nodes and analyze them with CCME, yielding results that reveal macro-features of the corresponding systems.
社区检测是在网络中对强连接节点进行分组的过程。许多针对加权网络的社区检测方法在空模型中有理论基础。因此，通过这些方法发现的社区具有统计学意义上的解释。在本文中，我们引入了一种称为连续配置模型的加权网络空模型。首先，我们提出了一种用于加权网络的社区提取算法，该算法在空模型下纳入了迭代假设检验。我们证明了边权和的中心极限定理以及该算法在加权随机块模型下的渐近一致性。然后，我们将该算法纳入一种称为CCME的社区检测方法中。为了对该方法进行基准测试，我们提供了一个模拟框架，该框架涉及空模型，以便在具有社区的加权网络中植入“背景”节点。我们表明，CCME在这些模拟中的实证性能与现有方法具有竞争力，特别是当存在重叠社区和背景节点时。为了进一步验证该方法，我们展示了两个具有潜在背景节点的真实世界网络，并用CCME对它们进行分析，得出的结果揭示了相应系统的宏观特征。
