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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. [ABSTRACT FROM AUTHOR]

In social network analysis, community detection on plain graphs has been widely studied. With the proliferation of available data, each user in the network is usually associated with additional attributes for elaborate description. However, many existing methods only concentrate on the topological structure and fail to deal with node‐attributed networks. These approaches are incapable of extracting clear semantic meanings for communities detected. In this paper, we combine the topological structure and attribute information into a unified process and propose a novel algorithm to detect overlapping semantic communities. Moreover, a new metric is designed to measure the density of semantic communities. The proposed algorithm is divided into 3 phases. First, we detect local semantic subcommunities from each node's perspective using a greedy strategy on the metric. Then, a supergraph, which consists of all these subcommunities is created. Finally, we find global semantic communities on the supergraph. The experimental results on real‐world data sets show the efficiency and effectiveness of our approach against other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]