Continual graph learning: A survey

Qiao Yuan

doi:10.1016/j.patcog.2026.113600

Continual graph learning: A survey

2026-03-23

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DOI：10.1016/j.patcog.2026.113600

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• Conducts in-depth analysis of CGL literature with extensive empirical comparisons. • Benchmarks effectiveness and efficiency of ten representative methods across five datasets, two GNN backbones, and two continual learning settings. • Lists representative datasets and open-source implementations for reproducibility. • Proposes a practical roadmap for developing continual graph learning methods.

Keywords:

Continual graph learning

Graph neural networks

Empirical benchmarking

Reproducibility

Methodology roadmap

Continual graph learning: A survey

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