mlsanityData to 7 Oct 2026

Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding

arXiv 2407.16959 ↗First posted 24 Jul 2024cs.LGcited by 6

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Abstract (arXiv)

Learning effective representations for Continuous-Time Dynamic Graphs (CTDGs) has garnered significant research interest, largely due to its powerful capabilities in modeling complex interactions between nodes. A fundamental and crucial requirement for representation learning in CTDGs is the appropriate estimation and preservation of proximity. However, due to the sparse and evolving characteristics of CTDGs, the spatial-temporal properties inherent in high-order proximity remain largely unexplored. Despite its importance, this property presents significant challenges due to the computationally intensive nature of personalized interaction intensity estimation and the dynamic attributes of CTDGs. To this end, we propose a novel Correlated Spatial-Temporal Positional encoding that incorporates a parameter-free personalized interaction intensity estimation under the weak assumption of the Poisson Point Process. Building on this, we introduce the Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (CorDGT), which efficiently retains the evolving spatial-temporal high-order proximity for effective node representation learning in CTDGs. Extensive experiments on seven small and two large-scale datasets demonstrate the superior performance and scalability of the proposed CorDGT. The code is available at: https://github.com/wangz3066/CorDGT.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 29 of its 54 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

29 of 54 references.

References · 29

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17 more
  1. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
  2. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  3. 2022GraphMAE: Self-Supervised Masked Graph Autoencoderscited by 443
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  6. 2020Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Predictioncited by 218
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  8. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representationscited by 5
  2. 2025Understanding and Improving Laplacian Positional Encodings For Temporal GNNscited by 3
  3. 2024Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Powercited by 0
  4. 2025MP-GFormer: A 3D-Geometry-Aware Dynamic Graph Transformer Approach for Machining Process Planningcited by 0
  5. 2026CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networkscited by 0
  6. 2026TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Predictioncited by 0