Network Alignment with Holistic Embeddings (Extended Abstract)

Network alignment is the task of identifying topologically 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 propose a novel end-to-end alignment framework that can leverage 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.