Pattern discovery plays an important part in the graph analysis process. Good examples are the detection of communities in social networks or the clustering into pathways of metabolic networks. However, elements may be shared by several clusters, making the patterns entangled. When mining such data, experts are usually interested in both each individual cluster and their overlaps. Dedicated visualization methods are therefore necessary to efficiently support their exploration process. In this article, we propose a new method that emphasizes patterns in a node-link diagram representation and allows to easily identify overlaps between these patterns as well. Our technique combines graph topology and embedding to compute concave hulls with holes surrounding the patterns of interest.