论文标题
动态和异构网络中的快速社区检测
Fast Community Detection in Dynamic and Heterogeneous Networks
论文作者
论文摘要
动态异质网络描述了不同类型的节点和边缘之间相互作用的时间演变。尽管有丰富的文献在动态网络中找到社区，但由于不同类型的节点和边缘的参与，这些方法在动态异质网络中的应用可能是不合适的，并且需要以不同的方式对待它们。在本文中，我们提出了一个统计框架，用于检测动态和异构网络中的共同社区。在此框架下，我们开发了一种称为DHNET的快速社区检测方法，该方法可以有效地估算社区标签以及社区的数量。 DHNET的一个有吸引力的特征是，它不需要先验地知道社区的数量，这是社区检测方法中的常见假设。虽然DHNET不需要在基础网络模型上进行任何参数假设，但我们表明，在具有时间相关结构和边缘稀疏性的随时间变化的异质随机块模型下，已确定的标签是一致的。我们进一步说明了通过模拟来说明DHNET的实用性以及审查Yelp数据的应用程序，其中DHNET在与现有解决方案相比的准确性和可解释性方面都显示出改进。
Dynamic heterogeneous networks describe the temporal evolution of interactions among nodes and edges of different types. While there is a rich literature on finding communities in dynamic networks, the application of these methods to dynamic heterogeneous networks can be inappropriate, due to the involvement of different types of nodes and edges and the need to treat them differently. In this paper, we propose a statistical framework for detecting common communities in dynamic and heterogeneous networks. Under this framework, we develop a fast community detection method called DHNet that can efficiently estimate the community label as well as the number of communities. An attractive feature of DHNet is that it does not require the number of communities to be known a priori, a common assumption in community detection methods. While DHNet does not require any parametric assumptions on the underlying network model, we show that the identified label is consistent under a time-varying heterogeneous stochastic block model with a temporal correlation structure and edge sparsity. We further illustrate the utility of DHNet through simulations and an application to review data from Yelp, where DHNet shows improvements both in terms of accuracy and interpretability over existing solutions.
