mlsanityData to 7 Oct 2026

Continual Learning on Graphs: Challenges, Solutions, and Opportunities

arXiv 2402.11565 ↗First posted 18 Feb 2024cs.LG cs.AIcited by 20

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Abstract (arXiv)

Continual learning on graph data has recently attracted paramount attention for its aim to resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially updated model to newly emerged graph tasks. While there have been efforts to summarize progress on continual learning research over Euclidean data, e.g., images and texts, a systematic review of progress in continual learning on graphs, a.k.a, continual graph learning (CGL) or lifelong graph learning, is still demanding. Graph data are far more complex in terms of data structures and application scenarios, making CGL task settings, model designs, and applications extremely challenging. To bridge the gap, we provide a comprehensive review of existing continual graph learning (CGL) algorithms by elucidating the different task settings and categorizing the existing methods based on their characteristics. We compare the CGL methods with traditional continual learning techniques and analyze the applicability of the traditional continual learning techniques to CGL tasks. Additionally, we review the benchmark works that are crucial to CGL research. Finally, we discuss the remaining challenges and propose several future directions. We will maintain an up-to-date GitHub repository featuring a comprehensive list of CGL algorithms, accessible at https://github.com/UConn-DSIS/Survey-of-Continual-Learning-on-Graphs.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 75 of its 122 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

75 of 122 references.

References · 75

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2016Learning without Forgettingcited by 2,465
  7. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  9. 2017Gradient Episodic Memory for Continual Learningcited by 1,871
  10. 2016Progressive Neural Networkscited by 1,332
  11. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  12. 2017Continual Learning with Deep Generative Replaycited by 1,152
38 more
  1. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,094
  2. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  3. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  4. 2020Dark Experience for General Continual Learning: a Strong, Simple Baselinecited by 895
  5. 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 734
  6. 2017Lifelong Learning with Dynamically Expandable Networkscited by 689
  7. 2020Benchmarking Graph Neural Networkscited by 672
  8. 2019Three scenarios for continual learningcited by 610
  9. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 471
  10. 2020Class-incremental learning: survey and performance evaluation on image classificationcited by 446
  11. 2019Provably Powerful Graph Networkscited by 385
  12. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  13. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  14. 2020Supermasks in Superpositioncited by 223
  15. 2019Meta-Learning Representations for Continual Learningcited by 189
  16. 2021Self-Supervised Learning of Graph Neural Networks: A Unified Reviewcited by 184
  17. 2014Graph-based Anomaly Detection and Description: A Surveycited by 182
  18. 2021A Comprehensive Survey of Scene Graphs: Generation and Applicationcited by 125
  19. 2019Continuous Graph Neural Networkscited by 114
  20. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  21. 2020A Systematic Survey on Deep Generative Models for Graph Generationcited by 94
  22. 2020Overcoming Catastrophic Forgetting in Graph Neural Networkscited by 93
  23. 2018Streaming Graph Neural Networkscited by 91
  24. 2020Line Graph Neural Networks for Link Predictioncited by 89
  25. 2021Few-Shot Graph Learning for Molecular Property Predictioncited by 81
  26. 2019Graph Few-shot Learning via Knowledge Transfercited by 63
  27. 2020Streaming Graph Neural Networks via Continual Learningcited by 63
  28. 2019Spherical Kernel for Efficient Graph Convolution on 3D Point Cloudscited by 57
  29. 2023CaT: Balanced Continual Graph Learning with Graph Condensationcited by 50
  30. 2020Graph Neural Networks with Continual Learning for Fake News Detection from Social Mediacited by 43
  31. 2020GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systemscited by 42
  32. 2021TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learningcited by 42
  33. 2021Graph Few-shot Class-incremental Learningcited by 40
  34. 2021Continual Learning of Knowledge Graph Embeddingscited by 35
  35. 2023Continual Learning on Dynamic Graphs via Parameter Isolationcited by 35
  36. 2022Self-Supervised Continual Graph Learning in Adaptive Riemannian Spacescited by 32
  37. 2020Lifelong Graph Learningcited by 31
  38. 2022Graph Lifelong Learning: A Surveycited by 30

50 most cited of 75 All 75 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 10; 2026: 5.

20 papers.

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  1. 2024Recent Advances of Multimodal Continual Learning: A Comprehensive Surveycited by 39
  2. 2024A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptationcited by 16
  3. 2024Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approachcited by 12
  4. 2025Learning without Isolation: Pathway Protection for Continual Learningcited by 6
  5. 2024Efficient and Robust Continual Graph Learning for Graph Classification in Biologycited by 3
  6. 2025Fairness in Augmented Graph Learning: A Surveycited by 2
  7. 2025Online Continual Graph Learningcited by 2
  8. 2024Learning System Dynamics without Forgettingcited by 1
  9. 2025Prompt-Driven Continual Graph Learningcited by 1
  10. 2025HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learningcited by 1
  11. 2025ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embeddingcited by 1
  12. 2025Tackling Distribution Shift in LLM via KILO: Knowledge-Instructed Learning for Continual Adaptationcited by 1
8 more
  1. 2025AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learningcited by 1
  2. 2026Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learningcited by 1
  3. 2026G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphscited by 1
  4. 2025Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Reviewcited by 0
  5. 2025The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learningcited by 0
  6. 2026UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learningcited by 0
  7. 2026DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shiftscited by 0
  8. 2026Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networkscited by 0