一个模型适配多种图数据，跨域预训练提升泛化能力
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
- 用专家库+门控机制，让单模型适应不同领域图数据
- 在链接预测和节点分类任务上均超越现有方法
- 适合缺乏图神经网络经验的从业者快速部署
图神经网络（GNN）在捕捉复杂网络模式方面表现卓越，已在多个领域取得成功。然而，现有GNN需针对每个数据集设计特定架构并从头训练，导致过程高度依赖专业知识，难以跨域泛化。因此，从业者难以判断哪个GNN模型能在其领域有效。为此，我们提出一种新型跨域预训练框架——“一个模型对应一个图”，突破以往方法无法用单一GNN捕捉显著差异领域图模式的局限。具体而言，我们预先训练一组专家模型，每个对应一个特定数据集；推理时，门控函数选择部分专家，有效融合已有知识，避免负迁移。大量实验一致表明，该方法在链接预测和节点分类任务上均表现更优。
原文摘要 · Abstract (English)
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.
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