Efficient cohesive subgraph search in big attributed graph data

Models for finding subgraph previously have focused on graphs having no attributes. However, these graphs provide only partial representation of real graph data and miss important attributes describing a variety of features for each vertex in the graphs. As such real graph data are better modelled as attributed graph. Investigations for cohesive subgraph search in attributed graphs are still at preliminary stage. Searching cohesive subgraphs in an attributed graph can discover communities and find useful information for keyword queries. In this thesis, several cohesive subgraph models considering spatial and textual attributes are studied, which fit into various real applications.