Continual Graph Learning: A Survey

📰 ArXiv cs.AI

Continual Graph Learning enables models to learn from streaming graph-structured data without forgetting previous knowledge

advanced Published 31 Mar 2026
Action Steps
  1. Implement experience replay to reuse past samples during training
  2. Use generative replay to synthesize informative subgraphs for rehearsal and address information loss and privacy risks
  3. Evaluate the effectiveness of different Continual Graph Learning approaches on streaming graph-structured data
Who Needs to Know This

Data scientists and AI engineers working on graph-based models can benefit from Continual Graph Learning to improve model performance and adapt to changing data streams

Key Insight

💡 Continual Graph Learning enables incremental learning from streaming graph-structured data without forgetting previous knowledge

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💡 Continual Graph Learning for streaming graph data

Key Takeaways

Continual Graph Learning enables models to learn from streaming graph-structured data without forgetting previous knowledge

Full Article

Title: Continual Graph Learning: A Survey

Abstract:
arXiv:2301.12230v2 Announce Type: replace-cross Abstract: Continual Graph Learning (CGL) enables models to incrementally learn from streaming graph-structured data without forgetting previously acquired knowledge. Experience replay is a common solution that reuses a subset of past samples during training. However, it may lead to information loss and privacy risks. Generative replay addresses these concerns by synthesizing informative subgraphs for rehearsal. Existing generative replay approaches
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