In a wide range of graph analysis tasks such as community detection and event detection, densest subgraph mining is important and primitive. With the development of social network, densest subgraph mining not only need to consider the structural data but also the attributes information, which descripts the features of nodes or edges. However, there are few researches on densest subgraph mining with attribute description. In this article, we only focus on the node-attributed graph. According to the properties of structure and attribute in node-attributed graphs, we define a novel dense subgraph pattern, called hybridized k-truss in attribute-augmented graph. A hybridized k-truss is a subgraph that consists of structural nodes and attribute nodes, of which there are at least (k − 2) common neighbors between any two connected nodes. We introduce the densest hybridized truss problem, and the densest hybridized truss mapping to a densely connected subgraph with homogenous attributes in the original graph. We propose a densest hybridized truss extraction (DHTE) algorithm for node-attributed graphs, to automatically find the densest subgraph with high density and homogenous attributes at the same time. Extensive experimental results of 21 real world datasets demonstrate the effectiveness and efficiency of DHTE over state-of-the-art methods, through comparison about structural cohesiveness and attributive homogeneity.

中文翻译：

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基于桁架的节点属性图中最密集的同构子图挖掘方法

在广泛的图分析任务（例如社区检测和事件检测）中，最密集的子图挖掘非常重要且原始。随着社交网络的发展，最密集的子图挖掘不仅需要考虑结构数据，还需要考虑属性信息，这些信息描述了节点或边的特征。但是，很少有关于具有属性描述的最密集子图挖掘的研究。在本文中，我们仅关注节点属性图。根据节点属性图中结构和属性的性质，我们定义了一种新颖的密集子图模式，称为属性增强图中的混合k桁架。混合k桁架是由结构节点和属性节点组成的子图，其中结构节点和属性节点至少有一个（k  -2）个任意两个相连节点之间的公共邻居。我们介绍了最密集的混合桁架问题，并将最密集的混合桁架映射到原始图中具有均匀属性的密集连接子图。我们为节点属性图提出了一种最密集的混合桁架提取（DHTE）算法，以自动同时找到具有高密度和同质属性的最密集子图。通过比较结构内聚性和属性同质性，21个现实世界数据集的大量实验结果证明了DHTE优于最新方法的有效性和效率。