mlsanityData to 7 Oct 2026

An Explainer for Temporal Graph Neural Networks

arXiv 2209.00807 ↗First posted 2 Sep 2022cs.LGcited by 12

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Abstract (arXiv)

Temporal graph neural networks (TGNNs) have been widely used for modeling time-evolving graph-related tasks due to their ability to capture both graph topology dependency and non-linear temporal dynamic. The explanation of TGNNs is of vital importance for a transparent and trustworthy model. However, the complex topology structure and temporal dependency make explaining TGNN models very challenging. In this paper, we propose a novel explainer framework for TGNN models. Given a time series on a graph to be explained, the framework can identify dominant explanations in the form of a probabilistic graphical model in a time period. Case studies on the transportation domain demonstrate that the proposed approach can discover dynamic dependency structures in a road network for a time period.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 2 Sep 2022.

Builds on

13 of 20 references.

References · 13

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  6. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  7. 2019Self-Attention Graph Poolingcited by 421
  8. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  9. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 324
  10. 2020PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networkscited by 210
  11. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  12. 2020Higher-Order Explanations of Graph Neural Networks via Relevant Walkscited by 138
1 more
  1. 2018MotifNet: a motif-based Graph Convolutional Network for directed graphscited by 63

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 4; 2025: 2; 2026: 1.

12 papers.

  1. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  2. 2023A Survey on Explainability of Graph Neural Networkscited by 28
  3. 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discoverycited by 27
  4. 2024Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detectioncited by 8
  5. 2023Structural Explanations for Graph Neural Networks using HSICcited by 4
  6. 2024Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphscited by 4
  7. 2024CoDy: Counterfactual Explainers for Dynamic Graphscited by 2
  8. 2023Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approachcited by 1
  9. 2025GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networkscited by 1
  10. 2024RW-NSGCN: A Robust Approach to Structural Attacks via Negative Samplingcited by 0
  11. 2025STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Modelscited by 0
  12. 2026Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attributioncited by 0