mlsanityData to 14 Oct 2026

Pre-Training on Dynamic Graph Neural Networks

arXiv 2102.12380 ↗First posted 24 Feb 2021cs.LG cs.SIcited by 11

Not on the map. Why?

Abstract (arXiv)

The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration. This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph. The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 29 of its 41 references that are in the map. First posted on arXiv 24 Feb 2021.

Builds on

29 of 41 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 2.

11 papers.

  1. 2021Graph Self-Supervised Learning: A Surveycited by 289
  2. 2021Self-supervised Learning on Graphs: Contrastive, Generative,or Predictivecited by 136
  3. 2024A survey of dynamic graph neural networkscited by 24
  4. 2025GRAVER: Generative Graph Vocabularies for Robust Graph Foundation Models Fine-tuningcited by 13
  5. 2024Node-Time Conditional Prompt Learning In Dynamic Graphscited by 11
  6. 2023CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networkscited by 6
  7. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 6
  8. 2025A Pre-training Framework for Relational Data with Information-theoretic Principlescited by 4
  9. 2026RAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generationcited by 4
  10. 2026Temporal Graph Pattern Machinecited by 3
  11. 2022Graph Representation Learning via Aggregation Enhancementcited by 0