mlsanityData to 14 Oct 2026

An Empirical Evaluation of Temporal Graph Benchmark

arXiv 2307.12510 ↗First posted 24 Jul 2023cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

In this paper, we conduct an empirical evaluation of Temporal Graph Benchmark (TGB) by extending our Dynamic Graph Library (DyGLib) to TGB. Compared with TGB, we include eleven popular dynamic graph learning methods for more exhaustive comparisons. Through the experiments, we find that (1) different models depict varying performance across various datasets, which is in line with previous observations; (2) the performance of some baselines can be significantly improved over the reported results in TGB when using DyGLib. This work aims to ease the researchers' efforts in evaluating various dynamic graph learning methods on TGB and attempts to offer results that can be directly referenced in the follow-up research. All the used resources in this project are publicly available at https://github.com/yule-BUAA/DyGLib_TGB. This work is in progress, and feedback from the community is welcomed for improvements.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 24 Jul 2023.

Builds on

12 of 15 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  3. 2020Inductive Representation Learning on Temporal Graphscited by 462
  4. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 448
  5. 2019Representation Learning for Dynamic Graphs: A Surveycited by 265
  6. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 193
  7. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 157
  8. 2023Do We Really Need Complicated Model Architectures For Temporal Networks?cited by 143
  9. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 138
  10. 2022Towards Better Evaluation for Dynamic Link Predictioncited by 122
  11. 2021TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learningcited by 68
  12. 2022Persistence in Complex Systemscited by 5

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Cited by year

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

8 papers.

  1. 2024Long Range Propagation on Continuous-Time Dynamic Graphscited by 28
  2. 2024DyG-Mamba: Continuous State Space Modeling on Dynamic Graphscited by 17
  3. 2024Repeat-Aware Neighbor Sampling for Dynamic Graph Learningcited by 11
  4. 2024Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identificationcited by 6
  5. 2023Temporal graph models fail to capture global temporal dynamicscited by 5
  6. 2025Future Link Prediction Without Memory or Aggregationcited by 5
  7. 2025TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Predictioncited by 2
  8. 2024Information propagation dynamics in Deep Graph Networkscited by 0