早退出图神经网络链接预测前沿论文，一文读懂方法创新与实验结论，适合AI研究者速览。
arXiv:2606.22167v1 Announce Type: new Abstract: Graph Neural Networks are great for link prediction in various network-like structures; however, the q…
动态图链接预测前沿模型，融合时间编码与SEAL框架，精准捕捉网络演化规律
arXiv:2602.14239v2 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network…
普通Transformer在链接预测任务上表现惊人，挑战了图神经网络在该领域的传统认知。
arXiv:2602.01553v2 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture…
这篇论文提出了用于链接预测的实例判别方法，在图学习领域有重要创新，适合研究人员快速了解前沿进展
arXiv:2605.20257v1 Announce Type: new Abstract: Recently, instance discrimination models have emerged as a major solution for self-supervised learning…
融合图结构与Seq2Seq模型，提出GA-S2S框架提升知识图谱链接预测效果，论文亮点突出。
arXiv:2605.18211v1 Announce Type: new Abstract: We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-sma…