Network clustering is a fundamental task that discovers innate communities or groups in networks. Hence, network clustering methods such as spectral clustering and regularized spectral clustering have been applied in a wide range of realms. On top of a network structure, it is known in social network analysis that incorporates information from each vertex can be beneficial. This has led to the development of a series of attributed network clustering algorithms that utilize not only network connectivity but also vertex covariates in order to uncover latent clusters. This paper compares the performance of state-of-the-art attributed network clustering approaches focused on detecting clusters of Seoul public bike stations. The data set consists of trip information over the bike station network in 2019. Spatial information about the bike stations is posed as vertex attributes. We show that certain attributed network clustering methods are well suited to detecting explainable clusters of bike rental stations. The results can help bike-sharing operators better understand system usage and learn how to improve service quality in the existing system.

中文翻译：

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分析首尔公共自行车站的属性网络聚类方法比较

网络聚类是发现网络中固有社区或群体的一项基本任务。因此，频谱聚类和正则化频谱聚类等网络聚类方法已在广泛的领域中得到应用。在网络结构之上，在社交网络分析中已知结合来自每个顶点的信息可能是有益的。这导致了一系列属性网络聚类算法的发展，这些算法不仅利用网络连通性，而且利用顶点协变量来发现潜在聚类。本文比较了专注于检测首尔公共自行车站集群的最先进的属性网络聚类方法的性能。该数据集包含 2019 年自行车站网络上的行程信息。关于自行车站的空间信息被设置为顶点属性。我们表明，某些属性网络聚类方法非常适合检测可解释的自行车租赁站集群。研究结果可以帮助共享单车运营商更好地了解系统使用情况，并了解如何提高现有系统的服务质量。