Community search over large semantic-based attribute graphs.

Community search has attracted widespread attention in many fields, such as protein interaction networks, social networks, and knowledge graphs. It aims to find cohesive subgraphs that are closely related to a query vertex q in a graph G. Existing community search researches based on attribute graphs rarely consider the semantic information of attributes and interpretability of the community. In this paper, we study Community Search over Semantic-based Attribute Graphs (CSSAG), where the attribute of each vertex in the graph G is a semantic graph. To guarantee both the attribute and structure cohesiveness of the community, we introduce the maximal common subgraph and minimal degree metric to measure the cohesiveness of the attribute and structure, respectively. In this way, we can get more understandable and diverse cohesive subgraphs as depicted in the experiment. Also, we design three different online query algorithms by integrating new pruning strategies to shift the search space. Extensive experiments on real-world networks show that our approaches are effective and efficient. [ABSTRACT FROM AUTHOR]