AGAR: an attribute-based graph refining method for community search

In many complex networks, there exist diverse network topologies as well as node attributes. However, the state-of-the-art community search methods which aim to find out communities containing query nodes, only consider the network topology but ignore the effect of node attributes. This may lead to the inaccuracy of communities, especially in sparse networks. In this paper, we propose an attribute-based graph refining method called AGAR for community search. Our method refines the graph topology based on both graph topologies and node attributes, and then helps community search methods obtain more accurate and meaningful communities. Experimental results on two real-world networks demonstrate AGAR can refine the initial graph into a more meaningful graph and help community search methods to find more accurate communities.