# Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures

arXiv: https://arxiv.org/abs/2002.12815 · HTML: https://mlsanity.com/paper/2002.12815/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 30 of 31)

- [Task-Adaptive Few-shot Node Classification](https://mlsanity.com/paper/2206.11972/) (2022)
- [Graph Few-shot Learning with Task-specific Structures](https://mlsanity.com/paper/2210.12130/) (2022)
- [FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs](https://mlsanity.com/paper/2205.02435/) (2022)
- [Cross-Domain Few-Shot Graph Classification](https://mlsanity.com/paper/2201.08265/) (2022)
- [A Survey of Data-Efficient Graph Learning](https://mlsanity.com/paper/2402.00447/) (2024)
- [Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning](https://mlsanity.com/paper/2206.04486/) (2022)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [Few-Shot Learning on Graphs](https://mlsanity.com/paper/2203.09308/) (2022)
- [What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data](https://mlsanity.com/paper/2106.06410/) (2021)
- [Meta-Learning with Graph Neural Networks: Methods and Applications](https://mlsanity.com/paper/2103.00137/) (2021)
- [RankDNN: Learning to Rank for Few-shot Learning](https://mlsanity.com/paper/2211.15320/) (2022)
- [Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training](https://mlsanity.com/paper/2407.14732/) (2024)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning](https://mlsanity.com/paper/2402.01440/) (2024)
- [BERTops: Studying BERT Representations under a Topological Lens](https://mlsanity.com/paper/2205.00953/) (2022)
- [Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data](https://mlsanity.com/paper/2404.12569/) (2024)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [Structure-Enhanced Meta-Learning For Few-Shot Graph Classification](https://mlsanity.com/paper/2103.03547/) (2021)
- [Metric Based Few-Shot Graph Classification](https://mlsanity.com/paper/2206.03695/) (2022)
- [Towards Few-shot Self-explaining Graph Neural Networks](https://mlsanity.com/paper/2408.07340/) (2024)
- [Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification](https://mlsanity.com/paper/2106.06873/) (2021)
- [Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression](https://mlsanity.com/paper/2209.13410/) (2022)
- [A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction](https://mlsanity.com/paper/2110.13280/) (2021)
- [Contrastive Graph Few-Shot Learning](https://mlsanity.com/paper/2210.00084/) (2022)
- [Dynamic Link Prediction for New Nodes in Temporal Graph Networks](https://mlsanity.com/paper/2310.09787/) (2023)
- [TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction](https://mlsanity.com/paper/2506.00453/) (2025)
- [Graph-level Neural Networks: Current Progress and Future Directions](https://mlsanity.com/paper/2205.15555/) (2022)
- [One shot learning based drivers head movement identification using a millimetre wave radar sensor](https://mlsanity.com/paper/2306.05291/) (2023)
- [Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study](https://mlsanity.com/paper/2311.12737/) (2023)
- [CPT: Competence-progressive Training Strategy for Few-shot Node Classification](https://mlsanity.com/paper/2402.00450/) (2024)

## Abstract (arXiv)

We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. Despite several interesting GNN variants being proposed recently for node and graph classification tasks, when faced with scarce labeled examples in the few shot setting, these GNNs exhibit significant loss in classification performance. Here, we present an approach where a probability measure is assigned to each graph based on the spectrum of the graphs normalized Laplacian. This enables us to accordingly cluster the graph base labels associated with each graph into super classes, where the Lp Wasserstein distance serves as our underlying distance metric. Subsequently, a super graph constructed based on the super classes is then fed to our proposed GNN framework which exploits the latent inter class relationships made explicit by the super graph to achieve better class label separation among the graphs. We conduct exhaustive empirical evaluations of our proposed method and show that it outperforms both the adaptation of state of the art graph classification methods to few shot scenario and our naive baseline GNNs. Additionally, we also extend and study the behavior of our method to semi supervised and active learning scenarios.