Network alignment is the task of identifying topo-logically and semantically similar nodes across (two) different networks. However, existing alignment models either cannot handle large-scale graphs or fail to leverage different types of network information or modalities. In this paper, we pro-pose a novel end-to-end alignment framework that can lever-age different modalities to compare and align network nodes in an efficient way. A comprehensive evaluation on various datasets shows that our technique outperforms state-of-the-art approaches. Our source code is available at https://github.com/thanhtrunghuynh93/holisticEmbeddingsNA.

Network alignment is the problem of pairing nodes between two graphs such that the paired nodes are structurally and semantically similar. A well-known application of network alignment is to identify which accounts in different social networks belong to the same person. Existing alignment techniques, however, lack scalability, cannot incorporate multi-dimensional information without training data, and are limited in the consistency constraints enforced by an alignment. In this paper, we propose a fully unsupervised network alignment framework based on a multi-order embedding model. The model learns the embeddings of each node using a graph convolutional neural representation, which we prove to satisfy consistency constraints. We further design a data augmentation method and a refinement mechanism to make the model adaptive to consistency violations and noise. Extensive experiments on real and synthetic datasets show that our model outperforms state-of-the-art alignment techniques. We also demonstrate the robustness of our model against adversarial conditions, such as structural noises, attribute noises, graph size imbalance, and hyper-parameter sensitivity.