Rock fractures are organized as networks with spatially varying arrangements. Due to networks' influence on bulk rock behaviour, it is important to quantify network spatial variation. We utilize an approach where fracture networks are treated as spatial graphs. By combining graph similarity measures with clustering techniques, spatial clusters within large-scale fracture networks are identified and organized hierarchically. The method is validated on a dataset with nearly 300 000 fractures.