mlsanityData to 7 Oct 2026

Deep learning for dynamic graphs: models and benchmarks

arXiv 2307.06104 ↗First posted 12 Jul 2023cs.LG cs.SIcited by 19

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Abstract (arXiv)

Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved. Specifically, there is an urge of making DGNs suitable for predictive tasks on realworld systems of interconnected entities, which evolve over time. With the aim of fostering research in the domain of dynamic graphs, at first, we survey recent advantages in learning both temporal and spatial information, providing a comprehensive overview of the current state-of-the-art in the domain of representation learning for dynamic graphs. Secondly, we conduct a fair performance comparison among the most popular proposed approaches on node and edge-level tasks, leveraging rigorous model selection and assessment for all the methods, thus establishing a sound baseline for evaluating new architectures and approaches

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 57 of its 100 references that are in the map. First posted on arXiv 12 Jul 2023.

Builds on

57 of 100 references.

References · 57

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 9; 2026: 2.

19 papers.

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  1. 2025On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learningcited by 42
  2. 2024TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphscited by 35
  3. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  4. 2024Long Range Propagation on Continuous-Time Dynamic Graphscited by 26
  5. 2025Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Taskscited by 12
  6. 2024Temporal Graph ODEs for Irregularly-Sampled Time Seriescited by 11
  7. 2025Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modelingcited by 9
  8. 2025Can You Hear Me Now? A Benchmark for Long-Range Graph Propagationcited by 8
  9. 2025Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamicscited by 6
  10. 2024Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networkscited by 4
  11. 2025What Do Temporal Graph Learning Models Learn?cited by 4
  12. 2024Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filteringcited by 3
7 more
  1. 2024Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hourscited by 2
  2. 2025Online Continual Graph Learningcited by 2
  3. 2024Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Reviewcited by 1
  4. 2025SeMob: Semantic Synthesis for Dynamic Urban Mobility Predictioncited by 1
  5. 2025Non-Dissipative Graph Propagation for Non-Local Community Detectioncited by 0
  6. 2026Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphscited by 0
  7. 2026A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Modelscited by 0