Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model

Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry. However, existing CTDG models encounter challenges stemming from noise and limited historical data.

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Yuxing Tian, Yiyan Qi, Aiwen Jiang, Qi Huang, Jian Guo (2024). Dataset: Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model. https://doi.org/10.57702/p9zv15q3

DOI retrieved: December 3, 2024

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Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2407.08500
Author Yuxing Tian
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Yiyan Qi
Aiwen Jiang
Qi Huang
Jian Guo