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Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects

arXiv 2403.13834 ↗First posted 10 Mar 2024cs.LGcited by 0

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Abstract (arXiv)

Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both academia and industry because prevailing studies on heterogeneous graphs often suffer from label sparsity. FLHG aims to tackle the performance degradation in the face of limited annotated data and there have been numerous recent studies proposing various methods and applications. In this paper, we provide a comprehensive review of existing FLHG methods, covering challenges, research progress, and future prospects. Specifically, we first formalize FLHG and categorize its methods into three types: single-heterogeneity FLHG, dual-heterogeneity FLHG, and multi-heterogeneity FLHG. Then, we analyze the research progress within each category, highlighting the most recent and representative developments. Finally, we identify and discuss promising directions for future research in FLHG. To the best of our knowledge, this paper is the first systematic and comprehensive review of FLHG.

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References · 31

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19 more
  1. 2020A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sourcescited by 126
  2. 2021Causal Attention for Interpretable and Generalizable Graph Classificationcited by 108
  3. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  4. 2018One-Shot Relational Learning for Knowledge Graphscited by 78
  5. 2019An Attention-based Graph Neural Network for Heterogeneous Structural Learningcited by 71
  6. 2022Simple and Efficient Heterogeneous Graph Neural Networkcited by 71
  7. 2021Few-shot Network Anomaly Detection via Cross-network Meta-learningcited by 64
  8. 2019Few-Shot Knowledge Graph Completioncited by 63
  9. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  10. 2021Property-Aware Relation Networks for Few-Shot Molecular Property Predictioncited by 41
  11. 2021Meta-Knowledge Transfer for Inductive Knowledge Graph Embeddingcited by 31
  12. 2019Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relationscited by 30
  13. 2021DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendationcited by 28
  14. 2022Few-Shot Learning on Graphscited by 11
  15. 2022Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networkscited by 10
  16. 2021Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learningcited by 9
  17. 2024Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphscited by 6
  18. 2022Few-Shot Semantic Relation Prediction across Heterogeneous Graphscited by 4
  19. 2023Cross-heterogeneity Graph Few-shot Learningcited by 3

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