Abstract
Graph neural networks (GNNs) have brought revolutionary advancements to the field of link prediction (LP), providing powerful tools for mining potential relationships in graphs. However, existing methods face challenges when dealing with large-scale sparse graphs and the need for a high degree of alignment between different datasets in transfer learning. Besides, although self-supervised methods have achieved remarkable success in many graph tasks, prior research has overlooked the potential of transfer learning to generalize across different graph datasets. To address these limitations, we propose a novel Graph Attention Adaptive Transfer Network (GAATNet). It combines the advantages of pre-training and fine-tuning to capture global node embedding information across datasets of different scales, ensuring efficient knowledge transfer and improved LP performance. To enhance the model's generalization ability and accelerate training, we design two key strategies: 1) Incorporate distant neighbor embeddings as biases in the self-attention module to capture global features. 2) Introduce a lightweight self-adapter module during fine-tuning to improve training efficiency. Comprehensive experiments on seven public datasets demonstrate that GAATNet achieves state-of-the-art performance in LP tasks. This study provides a general and scalable solution for LP tasks to effectively integrate GNNs with transfer learning. The source code and datasets are publicly available at this https URL
Abstract (translated)
图神经网络（GNNs）在链接预测（LP）领域带来了革命性的进步，为挖掘图形中潜在的关系提供了强大的工具。然而，现有的方法在处理大规模稀疏图和迁移学习中不同数据集之间高度对齐的需求时面临挑战。此外，尽管自监督方法在许多图任务上取得了显著成功，但之前的研究所忽视了迁移学习跨不同图数据集推广的潜力。为了解决这些限制，我们提出了一种新颖的图注意力适应性传输网络（GAATNet）。它结合预训练和微调的优势，捕获不同规模的数据集中节点嵌入信息的整体特性，确保有效的知识传递并提高LP性能。为了增强模型的泛化能力和加速训练过程，我们设计了两个关键策略：1）在自注意模块中将远距离邻居嵌入作为偏差以捕捉全局特征；2）在微调过程中引入一个轻量级自我适配器模块来提升训练效率。在七个公共数据集上的全面实验表明，GAATNet在LP任务上实现了最先进的性能。这项研究为LP任务提供了一种通用且可扩展的解决方案，能够有效地将GNNs与迁移学习相结合。源代码和数据集可以在以下网址获得：[此处应插入URL]。
URL
https://arxiv.org/abs/2512.22252
