Network clustering is an important problem thathas recently drawn a lot of attentions. Most existing workfocuses on clustering nodes within a single network. In manyapplications, however, there exist multiple related networks, inwhich each network may be constructed from a different domainand instances in one domain may be related to instances in otherdomains. In this paper, we propose a robust algorithm, MCA, formulti-network clustering that takes into account cross-domain relationshipsbetween instances. MCA has several advantages overthe existing single network clustering methods. First, it is ableto detect associations between clusters from different domains, which, however, is not addressed by any existing methods. Second, it achieves more consistent clustering results on multiple networksby leveraging the duality between clustering individual networksand inferring cross-network cluster alignment. Finally, it providesa multi-network clustering solution that is more robust to noiseand errors. We perform extensive experiments on a variety ofreal and synthetic networks to demonstrate the effectiveness andefficiency of MCA.