The problem of inter-individual comparison is of major importance in neuroimaging to detect patterns indicative of neurological pathology. Few works have been addressing the comparison of individual sulcal graphs in which variations across subjects manifest as changes in the number of nodes, graph topology and in the attributes that can be attached to nodes and edges. Here, we quantitatively evaluated different graph matching approaches in both the pairwise and multigraph matching frameworks, on synthetic graphs simulating the structure and attributes distributions of real data. Our results show that multigraph matching approach outperforms pairwise techniques in all simulations. The application to a set of real sulcal graphs from 134 subjects confirms this observation and demonstrates that multigraph matching approaches can scale and have a great potential in this context.