Faceted browsing has become a popular paradigm for user interfaces on the Web and has also been investigated in the context of RDF graphs. However, current faceted browsers for RDF graphs encounter performance issues when faced with two challenges: scale, where large datasets generate many results, and heterogeneity, where large numbers of properties and classes generate many facets. To address these challenges, we propose GRAFA: a faceted browsing system for heterogeneous large-scale RDF graphs based on a materialisation strategy that performs an offline analysis of the input graph in order to identify a subset of the exponential number of possible facet combinations that are candidates for indexing. In experiments over Wikidata, we demonstrate that materialisation allows for displaying (exact) faceted views over millions of diverse results in under a second while keeping index sizes relatively small. We also present initial usability studies over GRAFA.