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.
|Title of host publication||CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|Publication status||Published - 2022 Oct 17|
|Event||31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States|
Duration: 2022 Oct 17 → 2022 Oct 21
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||31st ACM International Conference on Information and Knowledge Management, CIKM 2022|
|Period||22/10/17 → 22/10/21|
Bibliographical noteFunding 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.
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Decision Sciences(all)