Finding communities that are not only relatively densely connected in a graph but that also show similar characteristics based on attribute information has drawn strong attention in the last years. There exists already a remarkable body of work that attempts to find communities in vertex-attributed graphs that are relatively homogeneous with respect to attribute values. Yet, it is scattered through different research fields and most of those publications fail to make the connection. In this paper, we identify important characteristics of the different approaches and place them into three broad categories: those that select descriptive attributes, related to clustering approaches, those that enumerate attribute-value combinations, related to pattern mining techniques, and those that identify conditional attribute weights, allowing for post-processing. We point out that the large majority of these techniques treat the same problem in terms of attribute representation, and are therefore interchangeable to a certain degree. In addition, different authors have found very similar algorithmic solutions to their respective problem.

在过去的几年中，寻找不仅在图表中相对密集地连接而且还显示出基于属性信息的相似特征的社区已引起了广泛的关注。已经存在大量的工作，试图在顶点属性图中找到相对于属性值而言相对同质的社区。但是，它散布在不同的研究领域，大多数出版物未能建立联系。在本文中，我们确定了不同方法的重要特征，并将其分为三大类：选择描述性属性的属性，与聚类方法相关的属性，枚举属性-值组合的属性，与模式挖掘技术以及用于识别条件属性权重的技术有关，从而可以进行后处理。我们指出，这些技术中的大多数都在属性表示方面处理相同的问题，因此在一定程度上可以互换。此外，不同的作者针对他们各自的问题发现了非常相似的算法解决方案。