mlsanityData to 7 Oct 2026

Efficient Dynamic Graph Representation Learning at Scale

arXiv 2112.07768 ↗First posted 14 Dec 2021cs.LG cs.AIcited by 8

Not on the map. Why?

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

21 of 40 references.

References · 21

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  4. 2018Self-Attentive Sequential Recommendationcited by 1,169
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  7. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  8. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  9. 2020Inductive Representation Learning on Temporal Graphscited by 454
  10. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  11. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 336
  12. 2020The Pushshift Reddit Datasetcited by 289
9 more
  1. 2018Adaptive Sampling Towards Fast Graph Representation Learningcited by 180
  2. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  3. 2016Discriminative Embeddings of Latent Variable Models for Structured Datacited by 130
  4. 2019Practice on Long Sequential User Behavior Modeling for Click-Through Rate Predictioncited by 118
  5. 2018Streaming Graph Neural Networkscited by 91
  6. 2017Geometric Matrix Completion with Recurrent Multi-Graph Neural Networkscited by 88
  7. 2018dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learningcited by 88
  8. 2016Deep Coevolutionary Network: Embedding User and Item Features for Recommendationcited by 33
  9. 2018DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networkscited by 9

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 2; 2025: 1.

8 papers.

  1. 2024Efficient Neural Common Neighbor for Temporal Graph Link Predictioncited by 18
  2. 2023TIGER: Temporal Interaction Graph Embedding with Restartscited by 17
  3. 2023DistTGL: Distributed Memory-Based Temporal Graph Neural Network Trainingcited by 12
  4. 2023Learning Dynamic Graph Embeddings with Neural Controlled Differential Equationscited by 7
  5. 2023SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embeddingcited by 7
  6. 2023iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservationcited by 4
  7. 2024Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hourscited by 2
  8. 2025HyperEvent: A Strong Baseline for Dynamic Link Prediction via Relative Structural Encodingcited by 1