# A survey of dynamic graph neural networks

arXiv: https://arxiv.org/abs/2404.18211 · HTML: https://mlsanity.com/paper/2404.18211/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 122 references that are in the map.
- First posted on arXiv 28 Apr 2024.

## Builds on (most cited first, 30 of 70)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs](https://mlsanity.com/paper/2103.09430/) (2021)
- [GPT-GNN: Generative Pre-Training of Graph Neural Networks](https://mlsanity.com/paper/2006.15437/) (2020)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [Net2Net: Accelerating Learning via Knowledge Transfer](https://mlsanity.com/paper/1511.05641/) (2015)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [GraphGPT: Graph Instruction Tuning for Large Language Models](https://mlsanity.com/paper/2310.13023/) (2023)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks](https://mlsanity.com/paper/2101.05974/) (2021)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017)
- [ROLAND: Graph Learning Framework for Dynamic Graphs](https://mlsanity.com/paper/2208.07239/) (2022)
- [Towards Better Evaluation for Dynamic Link Prediction](https://mlsanity.com/paper/2207.10128/) (2022)
- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://mlsanity.com/paper/1805.11273/) (2018)

## Built on by (most cited first, 24 of 24)

- [DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs](https://mlsanity.com/paper/2408.06966/) (2024)
- [TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics](https://mlsanity.com/paper/2502.02975/) (2025)
- [Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level](https://mlsanity.com/paper/2405.16405/) (2024)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Future Link Prediction Without Memory or Aggregation](https://mlsanity.com/paper/2505.19408/) (2025)
- [TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer](https://mlsanity.com/paper/2506.00431/) (2025)
- [A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2505.04461/) (2025)
- [What Do Temporal Graph Learning Models Learn?](https://mlsanity.com/paper/2510.09416/) (2025)
- [Temporal Graph Pattern Machine](https://mlsanity.com/paper/2601.22454/) (2026)
- [UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction](https://mlsanity.com/paper/2510.00457/) (2025)
- [Breaking Scale Anchoring: Frequency Representation Learning for Accurate High-Resolution Inference from Low-Resolution Training](https://mlsanity.com/paper/2512.05132/) (2025)
- [World-as-Graph: Relational World Modeling Through Latent Space Graphs](https://mlsanity.com/paper/2609.38927/) (2026)
- [When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction](https://mlsanity.com/paper/2507.13825/) (2025)
- [EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction](https://mlsanity.com/paper/2508.15378/) (2025)
- [Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Prediction](https://mlsanity.com/paper/2508.17554/) (2025)
- [Stochastic Event Prediction via Temporal Motif Transitions](https://mlsanity.com/paper/2603.05874/) (2026)
- [Lyapunov Stable Graph Neural Flow](https://mlsanity.com/paper/2603.12557/) (2026)
- [H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction](https://mlsanity.com/paper/2605.02150/) (2026)
- [AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2605.07121/) (2026)
- [PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning](https://mlsanity.com/paper/2606.30291/) (2026)
- [Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs](https://mlsanity.com/paper/2608.27948/) (2026)
- [STAR: Adaptive Spatial-Temporal Normalization for Unified Microservice Incident Management](https://mlsanity.com/paper/2609.31645/) (2026)
- [A temporal graph dissimilarity measure capturing epidemic spreading behavior](https://mlsanity.com/paper/2610.16456/) (2026)
- [Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification](https://mlsanity.com/paper/2610.17582/) (2026)

## Abstract (arXiv)

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time. By integrating sequence modeling modules into traditional GNN architectures, dynamic GNNs aim to bridge this gap, capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models. We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated. We also discuss large-scale dynamic GNNs and pre-training techniques. Although dynamic GNNs have shown superior performance, challenges remain in scalability, handling heterogeneous information, and lack of diverse graph datasets. The paper also discusses possible future directions, such as adaptive and memory-enhanced models, inductive learning, and theoretical analysis.