# Disentangle-based Continual Graph Representation Learning

arXiv: https://arxiv.org/abs/2010.02565 · HTML: https://mlsanity.com/paper/2010.02565/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [Continual Learning on Dynamic Graphs via Parameter Isolation](https://mlsanity.com/paper/2305.13825/) (2023)
- [Graph Lifelong Learning: A Survey](https://mlsanity.com/paper/2202.10688/) (2022)
- [Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs](https://mlsanity.com/paper/2211.15845/) (2022)
- [Continual Learning on Graphs: Challenges, Solutions, and Opportunities](https://mlsanity.com/paper/2402.11565/) (2024)
- [Continual Graph Learning: A Survey](https://mlsanity.com/paper/2301.12230/) (2023)
- [Towards Robust Graph Incremental Learning on Evolving Graphs](https://mlsanity.com/paper/2402.12987/) (2024)
- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [Towards Continual Knowledge Graph Embedding via Incremental Distillation](https://mlsanity.com/paper/2405.04453/) (2024)
- [Fast and Continual Knowledge Graph Embedding via Incremental LoRA](https://mlsanity.com/paper/2407.05705/) (2024)
- [Fair Graph Representation Learning via Sensitive Attribute Disentanglement](https://mlsanity.com/paper/2405.07011/) (2024)
- [Continual Learning on Graphs: A Survey](https://mlsanity.com/paper/2402.06330/) (2024)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [On the Limitation and Experience Replay for GNNs in Continual Learning](https://mlsanity.com/paper/2302.03534/) (2023)
- [NC-NCD: Novel Class Discovery for Node Classification](https://mlsanity.com/paper/2407.17816/) (2024)
- [Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks](https://mlsanity.com/paper/2407.19183/) (2024)
- [Neurosymbolic Methods for Dynamic Knowledge Graphs](https://mlsanity.com/paper/2409.04572/) (2024)
- [Learning to Evolve: Bayesian-Guided Continual Knowledge Graph Embedding](https://mlsanity.com/paper/2508.02426/) (2025)
- [When Modalities Remember: Continual Learning for Multimodal Knowledge Graphs](https://mlsanity.com/paper/2604.02778/) (2026)
- [When LLMs meet open-world graph learning: a new perspective for unlabeled data uncertainty](https://mlsanity.com/paper/2505.13989/) (2025)
- [Dynamic Mixture-of-Experts for Incremental Graph Learning](https://mlsanity.com/paper/2508.09974/) (2025)
- [Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognition](https://mlsanity.com/paper/2504.04963/) (2025)
- [SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding](https://mlsanity.com/paper/2508.11347/) (2025)
- [Matched Excess-Outranker Regularization for Candidate-Set Interference in Continual Knowledge Graph Embedding](https://mlsanity.com/paper/2608.24273/) (2026)
- [When Successful Knowledge Graph Edits Displace Correct Answers: Rank-Level Locality beyond Parameter Support](https://mlsanity.com/paper/2609.12116/) (2026)

## Abstract (arXiv)

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data. To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge. Moreover, we propose a disentangle-based continual graph representation learning (DiCGRL) framework inspired by the human's ability to learn procedural knowledge. The experimental results show that DiCGRL could effectively alleviate the catastrophic forgetting problem and outperform state-of-the-art continual learning models.