mlsanityData to 7 Oct 2026

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

arXiv 2103.03547 ↗First posted 5 Mar 2021cs.LGcited by 4

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Abstract (arXiv)

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction.Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage.This work explores the potential of metric-based meta-learning for solving few-shot graph classification.We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph. An implementation upon GIN, named SMF-GIN, is tested on two datasets, Chembl and TRIANGLES, where extensive experiments validate the effectiveness of the proposed method. The Chembl is constructed to fill in the gap of lacking large-scale benchmark for few-shot graph classification evaluation, which is released together with the implementation of SMF-GIN at: https://github.com/jiangshunyu/SMF-GIN.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 28 references that are in the map. First posted on arXiv 5 Mar 2021.

Builds on

23 of 28 references.

References · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2018How Powerful are Graph Neural Networks?cited by 4,355
  7. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  8. 2016Matching Networks for One Shot Learningcited by 2,246
  9. 2018Deep Graph Infomaxcited by 1,101
  10. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  11. 2019Strategies for Pre-training Graph Neural Networkscited by 964
  12. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
11 more
  1. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 544
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  3. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  4. 2018Meta-Transfer Learning for Few-Shot Learningcited by 306
  5. 2019A Baseline for Few-Shot Image Classificationcited by 298
  6. 2019SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learningcited by 194
  7. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  8. 2019Graph Few-shot Learning via Knowledge Transfercited by 63
  9. 2020Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measurescited by 31
  10. 2019Meta-Graph: Few Shot Link Prediction via Meta Learningcited by 24
  11. 2020Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classificationcited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  2. 2022Metric Based Few-Shot Graph Classificationcited by 4
  3. 2025Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibrationcited by 3
  4. 2022Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regressioncited by 2