Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the understanding of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on structural clustering. Yet, these algorithms have been evaluated on small graph database. In this paper, we propose DSCAN, a novel distributed structural graph clustering algorithm. We present an implementation of DSCAN on top of BLADYG, a distributed graph processing framework. We experimentally show that DSCAN significantly outperforms existing clustering algorithm in terms of scalability and performance in the case of large graphs.