In this paper, we present a subgroup discovery algorithm to find subgraphs in a graph that are associated with a given set of vertices. The association between a subgraph pattern and a set of vertices is defined by its significant enrichment based on a Bonferroni-corrected hypergeometric probability value. This interestingness measure requires a dedicated pruning procedure to limit the number of subgraph matches that must be calculated. The presented mining algorithm to find associated subgraph patterns in large graphs is therefore designed to efficiently traverse the search space. We demonstrate the operation of this algorithm by applying it on two biological graph data sets and show that we can find associated subgraphs for a biologically relevant set of vertices and that the found subgraphs themselves are biologically interesting.