Leveraging Attribute Interaction and Self-Training for Graph Alignment via Optimal Transport

Songyang Chen, Youfang Lin (), Ziyuan Zeng and Mengyang Xue
Additional contact information
Songyang Chen: School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Youfang Lin: School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Ziyuan Zeng: School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Mengyang Xue: School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2025, vol. 13, issue 12, 1-23

Abstract: Unsupervised alignment of two attributed graphs finds the node correspondence between them without any known anchor links. The recently proposed optimal transport (OT)-based approaches tackle this problem via Gromov–Wasserstein distance and joint learning of graph structures and node attributes, which achieve better accuracy and stability compared to previous embedding-based methods. However, it remains largely unexplored under the OT framework to fully utilize both structure and attribute information. We propose an Optimal Transport-based Graph Alignment method with Attribute Interaction and Self-Training ( PORTRAIT ), with the following two contributions. First, we enable the interaction of different dimensions of node attributes in the Gromov–Wasserstein learning process, while simultaneously integrating multi-layer graph structural information and node embeddings into the design of the intra-graph cost, which yields more expressive power with theoretical guarantee. Second, the self-training strategy is integrated into the OT-based learning process to significantly enhance node alignment accuracy with the help of confident predictions. Extensive experimental results validate the efficacy of the proposed model.

Keywords: unsupervised graph alignment; optimal transport; attribute interaction; self-training (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/12/1971/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/12/1971/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:12:p:1971-:d:1679353

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().