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Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study

arXiv 2311.12737 ↗First posted 21 Nov 2023cs.LGcited by 0

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Abstract (arXiv)

This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning. It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation, as well as the latest developments in few-shot learning, such as meta-learning and model-agnostic meta-learning. The paper explores these areas in depth and delves into further sub classifications. Rule based approaches and learning based approaches are surveyed under graph augmentation techniques. Few-Shot Learning on graphs is also studied in terms of metric learning techniques and optimization-based techniques. In all, this paper provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 21 Nov 2023.

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References · 26

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14 more
  1. 2022G-Mixup: Graph Data Augmentation for Graph Classificationcited by 146
  2. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  3. 2019Graph Adversarial Training: Dynamically Regularizing Based on Graph Structurecited by 93
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  5. 2022Graph Rationalization with Environment-based Augmentationscited by 70
  6. 2020Robust Optimization as Data Augmentation for Large-scale Graphscited by 58
  7. 2021Local Augmentation for Graph Neural Networkscited by 53
  8. 2020Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measurescited by 31
  9. 2021Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservationcited by 30
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  12. 2020M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classificationcited by 11
  13. 2021ifMixup: Interpolating Graph Pair to Regularize Graph Classificationcited by 7
  14. 2020Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classificationcited by 3

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