# Efficient Dynamic Graph Representation Learning at Scale

arXiv: https://arxiv.org/abs/2112.07768 · HTML: https://mlsanity.com/paper/2112.07768/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 21 of 21)

- [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)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Adaptive Sampling Towards Fast Graph Representation Learning](https://mlsanity.com/paper/1809.05343/) (2018)
- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017)
- [Discriminative Embeddings of Latent Variable Models for Structured Data](https://mlsanity.com/paper/1603.05629/) (2016)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Streaming Graph Neural Networks](https://mlsanity.com/paper/1810.10627/) (2018)
- [Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks](https://mlsanity.com/paper/1704.06803/) (2017)
- [dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning](https://mlsanity.com/paper/1809.02657/) (2018)
- [Deep Coevolutionary Network: Embedding User and Item Features for Recommendation](https://mlsanity.com/paper/1609.03675/) (2016)
- [DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networks](https://mlsanity.com/paper/1804.05755/) (2018)

## Built on by (most cited first, 8 of 8)

- [Efficient Neural Common Neighbor for Temporal Graph Link Prediction](https://mlsanity.com/paper/2406.07926/) (2024)
- [TIGER: Temporal Interaction Graph Embedding with Restarts](https://mlsanity.com/paper/2302.06057/) (2023)
- [DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training](https://mlsanity.com/paper/2307.07649/) (2023)
- [Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations](https://mlsanity.com/paper/2302.11354/) (2023)
- [SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding](https://mlsanity.com/paper/2308.14129/) (2023)
- [iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation](https://mlsanity.com/paper/2309.02012/) (2023)
- [Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours](https://mlsanity.com/paper/2412.20256/) (2024)
- [HyperEvent: A Strong Baseline for Dynamic Link Prediction via Relative Structural Encoding](https://mlsanity.com/paper/2507.11836/) (2025)

## Abstract (arXiv)

Dynamic graphs with ordered sequences of events between nodes are prevalent in real-world industrial applications such as e-commerce and social platforms. However, representation learning for dynamic graphs has posed great computational challenges due to the time and structure dependency and irregular nature of the data, preventing such models from being deployed to real-world applications. To tackle this challenge, we propose an efficient algorithm, Efficient Dynamic Graph lEarning (EDGE), which selectively expresses certain temporal dependency via training loss to improve the parallelism in computations. We show that EDGE can scale to dynamic graphs with millions of nodes and hundreds of millions of temporal events and achieve new state-of-the-art (SOTA) performance.