Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in dividing social actors (modelled as nodes in a social graph) with certain social connections (modelled as edges in the social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with the structure of the network and ignore features of the nodes, although major real-world networks provide additional actors' information such as age, gender, interests, etc., traditionally called node attributes. It is known that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This has led to a relatively novel direction in community detection --- constructing algorithms that use both the structure and the attributes of the network (modelled already via a node-attributed graph) to yield more informative and qualitative results. During the last decade many methods based on different ideas and techniques have appeared in this direction. Although there exist some partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause uncertainty in practice. In this paper we aim at clarifying the overall situation by proposing a clear classification of the methods and providing a comprehensive survey of the available results. We not only group and analyse the corresponding methods but also focus on practical aspects, including the information which methods outperform others and which datasets and quality measures are used for evaluation.

本文介绍了社区检测的基本问题，即如何把社交网络中具有明确社交关系的社会行为者分成紧密相连和高度相关的群体，并详细介绍了使用节点属性的方法，对已知的方法进行分类，提供了每种方法的一般技术思想，分析了当前领域的状况并揭示了一些需要未来解决的问题。

节点属性社交网络中的社区发现：综述