Network alignment is the matching of two networks with corresponding nodes that belong to the same user or entity. The most common application is to analyze which accounts belong to the same user in two social networks. Most of existing techniques rely on matrix factorization so that they cannot be scaled to large-scale networks, are constrained by strict constraints, and cannot learn node embedding without a training set. In this paper, we propose an unsupervised network alignment model based on multi-level graph attention networks. The model uses multi-level graph attention network to learn the embedded representation of nodes, satisfying attribute and structure constraints of alignment. Augmented learning process is proposed to simulate attribute noise and structural noise to improve adaptability of the model. Extensive experiments on real datasets show that the proposed model performs better than the state-of-the-art network alignment model. We also demonstrate the robustness of the proposed model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.