Semantic community query in a large-scale attributed graph based on an attribute cohesiveness optimization strategy

The task of a semantic community query is to obtain a subgraph S based on a given query vertex q (or vertex set) and other query parameters in an attributed graph G such that S belongs to G, contains q and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named SCQ in a large-scale attributed graph. First, the k-core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large-scale attributed graphs, SCQ applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large-scale attributed graphs.