K-Truss Based Top-Communities Search in Large Graphs

The study of community search problem subjected to find densely connected subgraphs that satisfy the query conditions in social and information networks has attracted many researchers. In most real life networks a query node normally involves in several communities, therefore, most likely the same query results into communities of different densities. In this paper, based on the concept of k-truss, which is a type of cohesive subgraph, we propose linear-time algorithms to find the top-r k-truss communities containing a query node in large graphs. We adapt some optimal index construction strategies which can support efficient search of top-r k-truss communities with a linear cost to work for top-r community search. We carry out extensive experiments on real-world large networks, and the results signify the efficiency and effectiveness of the proposed algorithms.