提出GAATNet模型，提升跨数据集链接预测的迁移效率与性能
Graph Attention-based Adaptive Transfer Learning for Link Prediction
- 基于图注意力机制设计自适应迁移网络，融合预训练与微调优势
- 在7个公开数据集上达到当前最优链接预测效果，显著提升泛化能力
- 适合需要高效迁移学习的大型稀疏图场景，如社交网络分析
图神经网络（GNN）在链接预测（LP）任务中取得革命性进展，但现有方法在处理大规模稀疏图及跨数据集迁移学习时仍面临挑战。尽管自监督方法在多种图任务中表现优异，但此前研究忽视了迁移学习在不同图数据集间的泛化潜力。为此，本文提出图注意力自适应迁移网络（GAATNet），结合预训练与微调优势，捕捉跨不同规模数据集的全局节点嵌入信息，实现高效知识迁移并提升LP性能。为增强模型泛化能力并加速训练，设计两项关键策略：1）在自注意力模块中引入远距离邻居嵌入作为偏置，捕获全局特征；2）在微调阶段加入轻量级自适配模块，提高训练效率。在7个公开数据集上的综合实验表明，GAATNet在链接预测任务中达到领先性能。本研究为链接预测提供了通用且可扩展的GNN与迁移学习融合方案。源代码与数据集已公开于https://github.com/DSI-Lab1/GAATNet。
原文摘要 · Abstract (English)
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 https://github.com/DSI-Lab1/GAATNet
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