Self-supervised temporal graph learning with temporal and structural intensity alignment

M Liu, K Liang, Y Zhao, W Tu, S Zhou… - … on Neural Networks …, 2024 - ieeexplore.ieee.org
Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
被引用次数：29 相关文章

Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

M Liu, K Liang, Y Zhao, W Tu, S Zhou, X Liu… - arXiv preprint arXiv …, 2023 - arxiv.org
Temporal graph learning aims to generate high-quality representations for graph-based
tasks along with dynamic information, which has recently drawn increasing attention. Unlike …

Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment

M Liu, K Liang, Y Zhao, W Tu, S Zhou, X Liu… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Temporal graph learning aims to generate high-quality representations for graph-based
tasks along with dynamic information, which has recently drawn increasing attention. Unlike …

Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment.

M Liu, K Liang, Y Zhao, W Tu, S Zhou… - IEEE Transactions on …, 2024 - europepmc.org
Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment

M Liu, K Liang, Y Zhao, W Tu, S Zhou… - IEEE transactions on … - pubmed.ncbi.nlm.nih.gov
Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …