Community detection is a fundamental problem in social network analysis consisting, roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social graph) with certain social connections (modeled 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 (traditionally called node attributes), although the majority of real-world social networks provide additional actors’ information such as age, gender, interests, etc. It is believed that the attributes may clarify and enrich the knowledge about the actors and give sense to the detected communities. This belief has motivated the progress in developing community detection methods that use both the structure and the attributes of the network (modeled already via a node-attributed graph) to yield more informative and qualitative community detection results.

During the last decade many such methods based on different ideas and techniques have appeared. Although there exist partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause repetitions in methodology and uncertainty in practice.

In this paper we aim at describing and clarifying the overall situation in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how the structure and the attributes are fused. We not only give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available information which methods outperform others and which datasets and quality measures are used for their performance evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future.

社区检测是社交网络分析中的一个基本问题，粗略地说，就是将具有某些社交关系（建模为社交图的边缘）的无所作为的社会行为者（建模为社交图的节点）划分为紧密编织且高度相关的群体，每个小组都与其他小组分开。传统的社区检测方法通常只处理网络的结构，而忽略节点的特征（传统上称为节点属性），尽管大多数现实世界中的社交网络都提供了其他参与者的信息，例如年龄，性别，兴趣等。可以相信，这些属性可以澄清和丰富有关演员的知识，并使被发现的社区有意义。

在过去的十年中，出现了许多基于不同思想和技术的方法。尽管存在部分概述，但最近的调查是必要的，因为越来越多的方法可能导致方法的重复和实践中的不确定性。

在本文中，我们旨在描述和阐明节点归因社交网络中社区检测领域的总体情况。即，我们对已知方法进行详尽的搜索，并根据何时以及如何融合结构和属性来对它们进行分类。我们不仅提供每个类的描述，还提供该类中每个方法背后的一般技术思想。此外，我们关注可用信息，哪些方法优于其他方法，以及哪些数据集和质量度量用于其性能评估。根据收集到的信息，我们对本领域的现状做出结论，并披露一些似乎很重要的问题，这些问题在将来需要解决。