GradAlign+: Empowering Gradual Network Alignment Using Attribute Augmentation

Jin Duk Park, Cong Tran, Won Yong Shin, Xin Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Network alignment (NA) is the task of discovering node correspondences across different networks. Although NA methods have achieved remarkable success in a myriad of scenarios, their satisfactory performance is not without prior anchor link information and/or node attributes, which may not always be available. In this paper, we propose Grad-Align+, a novel NA method using node attribute augmentation that is quite robust to the absence of such additional information. Grad-Align+ is built upon a recent state-of-the-art NA method, the so-called Grad-Align, that gradually discovers only a part of node pairs until all node pairs are found. Specifically, Grad-Align+ is composed of the following key components: 1) augmenting node attributes based on nodes' centrality measures, 2) calculating an embedding similarity matrix extracted from a graph neural network into which the augmented node attributes are fed, and 3) gradually discovering node pairs by calculating similarities between cross-network nodes with respect to the aligned cross-network neighbor-pair. Experimental results demonstrate that Grad-Align+ exhibits (a) superiority over benchmark NA methods, (b) empirical validation of our theoretical findings, and (c) the effectiveness of our attribute augmentation module.

Original languageEnglish
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450392365
Publication statusPublished - 2022 Oct 17
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: 2022 Oct 172022 Oct 21

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
The work of W.-Y. Shin was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C3004345). The work of X. Cao was supported by ARC DE190100663.

Publisher Copyright:
© 2022 ACM.

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)


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