Leveraging Attribute Interaction and Self-Training for Graph Alignment via Optimal Transport Article Swipe
Songyang Chen
,
Youfang Lin
,
Zhoumo Zeng
,
Mengyang Xue
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13121971
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13121971
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math13121971
- https://www.mdpi.com/2227-7390/13/12/1971/pdf?version=1750067447
- OA Status
- gold
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411327855
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https://openalex.org/W4411327855Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math13121971Digital Object Identifier
- Title
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Leveraging Attribute Interaction and Self-Training for Graph Alignment via Optimal TransportWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-15Full publication date if available
- Authors
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Songyang Chen, Youfang Lin, Zhoumo Zeng, Mengyang XueList of authors in order
- Landing page
-
https://doi.org/10.3390/math13121971Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7390/13/12/1971/pdf?version=1750067447Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/13/12/1971/pdf?version=1750067447Direct OA link when available
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Computer science, Graph, Training (meteorology), Artificial intelligence, Human–computer interaction, Theoretical computer science, Physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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45Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.largely | 53 |
| abstract_inverted_index.optimal | 20 |
| abstract_inverted_index.problem | 26 |
| abstract_inverted_index.process | 137 |
| abstract_inverted_index.propose | 68 |
| abstract_inverted_index.remains | 52 |
| abstract_inverted_index.results | 152 |
| abstract_inverted_index.utilize | 61 |
| abstract_inverted_index.without | 12 |
| abstract_inverted_index.However, | 50 |
| abstract_inverted_index.OT-based | 135 |
| abstract_inverted_index.accuracy | 42, 143 |
| abstract_inverted_index.compared | 45 |
| abstract_inverted_index.distance | 29 |
| abstract_inverted_index.efficacy | 155 |
| abstract_inverted_index.learning | 32, 100, 136 |
| abstract_inverted_index.methods. | 49 |
| abstract_inverted_index.previous | 47 |
| abstract_inverted_index.process, | 101 |
| abstract_inverted_index.proposed | 19, 158 |
| abstract_inverted_index.recently | 18 |
| abstract_inverted_index.strategy | 130 |
| abstract_inverted_index.validate | 153 |
| abstract_inverted_index.Alignment | 73 |
| abstract_inverted_index.Attribute | 76 |
| abstract_inverted_index.Extensive | 150 |
| abstract_inverted_index.alignment | 1, 142 |
| abstract_inverted_index.attribute | 65 |
| abstract_inverted_index.confident | 148 |
| abstract_inverted_index.different | 92 |
| abstract_inverted_index.following | 83 |
| abstract_inverted_index.framework | 58 |
| abstract_inverted_index.stability | 44 |
| abstract_inverted_index.structure | 63 |
| abstract_inverted_index.transport | 21 |
| abstract_inverted_index.(OT)-based | 22 |
| abstract_inverted_index.approaches | 23 |
| abstract_inverted_index.attributed | 4 |
| abstract_inverted_index.attributes | 96 |
| abstract_inverted_index.dimensions | 93 |
| abstract_inverted_index.embeddings | 111 |
| abstract_inverted_index.expressive | 122 |
| abstract_inverted_index.guarantee. | 126 |
| abstract_inverted_index.integrated | 132 |
| abstract_inverted_index.structural | 107 |
| abstract_inverted_index.structures | 35 |
| abstract_inverted_index.unexplored | 54 |
| abstract_inverted_index.(PORTRAIT), | 80 |
| abstract_inverted_index.Interaction | 77 |
| abstract_inverted_index.attributes, | 38 |
| abstract_inverted_index.information | 108 |
| abstract_inverted_index.integrating | 104 |
| abstract_inverted_index.interaction | 90 |
| abstract_inverted_index.intra-graph | 117 |
| abstract_inverted_index.multi-layer | 105 |
| abstract_inverted_index.theoretical | 125 |
| abstract_inverted_index.Unsupervised | 0 |
| abstract_inverted_index.experimental | 151 |
| abstract_inverted_index.information. | 66 |
| abstract_inverted_index.predictions. | 149 |
| abstract_inverted_index.Self-Training | 79 |
| abstract_inverted_index.self-training | 129 |
| abstract_inverted_index.significantly | 139 |
| abstract_inverted_index.contributions. | 85 |
| abstract_inverted_index.correspondence | 9 |
| abstract_inverted_index.simultaneously | 103 |
| abstract_inverted_index.Transport-based | 71 |
| abstract_inverted_index.embedding-based | 48 |
| abstract_inverted_index.Gromov–Wasserstein | 28, 99 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5013548083 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I21193070 |
| citation_normalized_percentile.value | 0.09907036 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |