We introduce the novel concept of graph alignment, a generalization of graph isomorphism that is motivated by the commonly used multiple sequence alignments. Graph alignments and graph isomorphisms are equivalent in the case of pairwise comparisons, but if many graphs should be analyzed simultaneously, graph alignments are more robust against noise and perturbations. Thus, graph alignments can detect conserved patterns in large sets of related graphs in a more reliable way.