Grad-Align: Gradual Network Alignment via Graph Neural Networks (Student Abstract)


  • Jin-Duk Park Yonsei University
  • Cong Tran Posts and Telecommunications Institute of Technology
  • Won-Yong Shin Yonsei University
  • Xin Cao The University of New South Wales



Network Alignment, Gradual Alignment, Graph Neural Network, Network Embedding, Similarity Measure


Network alignment (NA) is the task of finding the correspondence of nodes between two networks. Since most existing NA methods have attempted to discover every node pair at once, they may fail to utilize node pairs that have strong consistency across different networks in the NA task. To tackle this challenge, we propose Grad-Align, a new NA method that gradually discovers node pairs by making full use of either node pairs exhibiting strong consistency or prior matching information. Specifically, the proposed method gradually aligns nodes based on both the similarity of embeddings generated using graph neural networks (GNNs) and the Tversky similarity, which is an asymmetric set similarity using the Tversky index applicable to networks with different scales. Experimental evaluation demonstrates that Grad-Align consistently outperforms state-of-the-art NA methods in terms of the alignment accuracy. Our source code is available at




How to Cite

Park, J.-D., Tran, C., Shin, W.-Y., & Cao, X. (2022). Grad-Align: Gradual Network Alignment via Graph Neural Networks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13027-13028.