# Topology-aware Embedding Memory for Continual Learning on Expanding Networks

arXiv: https://arxiv.org/abs/2401.13200 · HTML: https://mlsanity.com/paper/2401.13200/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 57 of its 100 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 30 of 57)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Orthogonal Gradient Descent for Continual Learning](https://mlsanity.com/paper/1910.07104/) (2019)
- [SIGN: Scalable Inception Graph Neural Networks](https://mlsanity.com/paper/2004.11198/) (2020)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Supermasks in Superposition](https://mlsanity.com/paper/2006.14769/) (2020)
- [Graph Neural Networks for Graphs with Heterophily: A Survey](https://mlsanity.com/paper/2202.07082/) (2022)
- [Scalable and Order-robust Continual Learning with Additive Parameter Decomposition](https://mlsanity.com/paper/1902.09432/) (2019)
- [Meta-GNN: On Few-shot Node Classification in Graph Meta-learning](https://mlsanity.com/paper/1905.09718/) (2019)
- [Overcoming Catastrophic Forgetting in Graph Neural Networks](https://mlsanity.com/paper/2012.06002/) (2020)
- [Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis](https://mlsanity.com/paper/1909.01520/) (2019)

## Built on by (most cited first, 14 of 14)

- [Towards Continuous Reuse of Graph Models via Holistic Memory Diversification](https://mlsanity.com/paper/2406.07413/) (2024)
- [Federated Continual Graph Learning](https://mlsanity.com/paper/2411.18919/) (2024)
- [Online Continual Graph Learning](https://mlsanity.com/paper/2508.03283/) (2025)
- [Algorithm Design for Continual Learning in IoT Networks](https://mlsanity.com/paper/2412.16830/) (2024)
- [HERO: Preserving Structure and Semantics in Heterogeneous Continual Graph Learning](https://mlsanity.com/paper/2505.17458/) (2025)
- [Dynamic Mixture-of-Experts for Incremental Graph Learning](https://mlsanity.com/paper/2508.09974/) (2025)
- [The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning](https://mlsanity.com/paper/2510.06819/) (2025)
- [TAAM:Inductive Graph-Class Incremental Learning with Task-Aware Adaptive Modulation](https://mlsanity.com/paper/2602.08036/) (2026)
- [Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering](https://mlsanity.com/paper/2603.19501/) (2026)
- [Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay](https://mlsanity.com/paper/2604.14259/) (2026)
- [CoMemNet: A Continual Memory Network with Drift-Aware Sampling for Traffic Prediction](https://mlsanity.com/paper/2605.05738/) (2026)
- [DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts](https://mlsanity.com/paper/2605.12998/) (2026)
- [SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport](https://mlsanity.com/paper/2607.00377/) (2026)
- [CA-DGCL: Dynamic Graph Continual Learning via Condensation and Attachment](https://mlsanity.com/paper/2607.11112/) (2026)

## Abstract (arXiv)

Memory replay based techniques have shown great success for continual learning with incrementally accumulated Euclidean data. Directly applying them to continually expanding networks, however, leads to the potential memory explosion problem due to the need to buffer representative nodes and their associated topological neighborhood structures. To this end, we systematically analyze the key challenges in the memory explosion problem, and present a general framework, textiti.e., Parameter Decoupled Graph Neural Networks (PDGNNs) with Topology-aware Embedding Memory (TEM), to tackle this issue. The proposed framework not only reduces the memory space complexity from O(ndL) to O(n)~footnoten: memory budget, d: average node degree, L: the radius of the GNN receptive field, but also fully utilizes the topological information for memory replay. Specifically, PDGNNs decouple trainable parameters from the computation ego-subnetwork via textitTopology-aware Embeddings (TEs), which compress ego-subnetworks into compact vectors (textiti.e., TEs) to reduce the memory consumption. Based on this framework, we discover a unique textitpseudo-training effect in continual learning on expanding networks and this effect motivates us to develop a novel textitcoverage maximization sampling strategy that can enhance the performance with a tight memory budget. Thorough empirical studies demonstrate that, by tackling the memory explosion problem and incorporating topological information into memory replay, PDGNNs with TEM significantly outperform state-of-the-art techniques, especially in the challenging class-incremental setting.