mlsanityData to 14 Oct 2026

Graph-Based Continual Learning

arXiv 2007.04813 ↗First posted 9 Jul 2020stat.ML cs.LGcited by 24

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Abstract (arXiv)

Despite significant advances, continual learning models still suffer from catastrophic forgetting when exposed to incrementally available data from non-stationary distributions. Rehearsal approaches alleviate the problem by maintaining and replaying a small episodic memory of previous samples, often implemented as an array of independent memory slots. In this work, we propose to augment such an array with a learnable random graph that captures pairwise similarities between its samples, and use it not only to learn new tasks but also to guard against forgetting. Empirical results on several benchmark datasets show that our model consistently outperforms recently proposed baselines for task-free continual learning.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jul 2020.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 6; 2024: 5; 2025: 2.

24 papers.

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  1. 2021New Insights on Reducing Abrupt Representation Change in Online Continual Learningcited by 219
  2. 2021Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learningcited by 72
  3. 2021Recent Advances of Continual Learning in Computer Vision: An Overviewcited by 55
  4. 2021Graph Few-shot Class-incremental Learningcited by 41
  5. 2021Continual Learning for Recurrent Neural Networks: an Empirical Evaluationcited by 34
  6. 2021Continual Learning on Noisy Data Streams via Self-Purified Replaycited by 27
  7. 2022How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Compositioncited by 27
  8. 2022The Neural Process Family: Survey, Applications and Perspectivescited by 23
  9. 2024Continual Learning on Graphs: Challenges, Solutions, and Opportunitiescited by 21
  10. 2023New Insights for the Stability-Plasticity Dilemma in Online Continual Learningcited by 16
  11. 2023Mirage: Model-Agnostic Graph Distillation for Graph Classificationcited by 13
  12. 2022Optimizing Class Distribution in Memory for Multi-Label Online Continual Learningcited by 12
12 more
  1. 2023Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learningcited by 11
  2. 2024A Topology-aware Graph Coarsening Framework for Continual Graph Learningcited by 11
  3. 2024Towards Continuous Reuse of Graph Models via Holistic Memory Diversificationcited by 7
  4. 2023Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapterscited by 5
  5. 2023History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completioncited by 5
  6. 2022Rethinking Task-Incremental Learning Baselinescited by 4
  7. 2024AGALE: A Graph-Aware Continual Learning Evaluation Frameworkcited by 2
  8. 2024Graph Mining under Data scarcitycited by 1
  9. 2025When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertaintycited by 1
  10. 2022New Insights on Reducing Abrupt Representation Change in Online Continual Learningcited by 0
  11. 2023Learning Continually on a Sequence of Graphs -- The Dynamical System Waycited by 0
  12. 2025Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Samplingcited by 0