Virtual Node Tuning for Few-shot Node Classification Article Swipe
Zhen Tan
,
Ruocheng Guo
,
Kaize Ding
,
Huan Liu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.06063
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.06063
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer structural knowledge from base classes with abundant labels to target novel classes. However, existing solutions become ineffective or inapplicable when base classes have no or limited labeled nodes. To address this challenge, we propose an innovative method dubbed Virtual Node Tuning (VNT). Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task. A unique feature of VNT is that, by incorporating a Graph-based Pseudo Prompt Evolution (GPPE) module, VNT-GPPE can handle scenarios with sparse labels in base classes. Experimental results on four datasets demonstrate the superiority of the proposed approach in addressing FSNC with unlabeled or sparsely labeled base classes, outperforming existing state-of-the-art methods and even fully supervised baselines.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.06063
- https://arxiv.org/pdf/2306.06063
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380374855
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380374855Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2306.06063Digital Object Identifier
- Title
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Virtual Node Tuning for Few-shot Node ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-09Full publication date if available
- Authors
-
Zhen Tan, Ruocheng Guo, Kaize Ding, Huan LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.06063Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.06063Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2306.06063Direct OA link when available
- Concepts
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Computer science, Encoder, Graph, Embedding, Node (physics), Artificial intelligence, Feature learning, One shot, Graph embedding, Class (philosophy), Feature vector, Machine learning, Theoretical computer science, Engineering, Operating system, Mechanical engineering, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.structural | 33 |
| abstract_inverted_index.supervised | 170 |
| abstract_inverted_index.Graph-based | 125 |
| abstract_inverted_index.demonstrate | 146 |
| abstract_inverted_index.ineffective | 49 |
| abstract_inverted_index.superiority | 148 |
| abstract_inverted_index.transformer | 81 |
| abstract_inverted_index.Experimental | 141 |
| abstract_inverted_index.inapplicable | 51 |
| abstract_inverted_index.incorporating | 123 |
| abstract_inverted_index.meta-learning | 27 |
| abstract_inverted_index.outperforming | 163 |
| abstract_inverted_index.Classification | 2 |
| abstract_inverted_index.representation | 9 |
| abstract_inverted_index.state-of-the-art | 165 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |