Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate network patterns, achieving success across different domains. However, existing GNNs require careful domain-specific architecture designs and training from scratch on each dataset, leading to an expertise-intensive process with difficulty in generalizing across graphs from different domains. Therefore, it can be hard for practitioners to infer which GNN model can generalize well to graphs from their domains. To address this challenge, we propose a novel cross-domain pretraining framework, "one model for one graph," which overcomes the limitations of previous approaches that failed to use a single GNN to capture diverse graph patterns across domains with significant gaps. Specifically, we pretrain a bank of expert models, with each one corresponding to a specific dataset. When inferring to a new graph, gating functions choose a subset of experts to effectively integrate prior model knowledge while avoiding negative transfer. Extensive experiments consistently demonstrate the superiority of our proposed method on both link prediction and node classification tasks.

本研究针对现有图神经网络在不同领域图形上的泛化能力不足问题，提出了一种新颖的跨领域预训练框架“单模型针对单图”。该方法通过预训练专家模型以捕捉不同图形模式，显著提高了在链接预测和节点分类任务上的性能，展示了其在跨领域应用中的潜力。

一种针对单一图形的模型：跨领域图形预训练的新视角