GraphBridge让图神经网络跨任务跨领域迁移，无需修改结构或配置。
GraphBridge: Towards Arbitrary Transfer Learning in GNNs
- 通过桥接网络连接预训练模型与新任务头，实现任意维度输出。
- 在16个数据集上验证，支持图到图、节点到节点等多类迁移任务。
- 融合源模型与协同训练模型，减少负迁移，适合跨域应用者使用。
图神经网络（GNN）传统上需针对每个领域和任务单独训练，难以将知识迁移到异构数据设置中。本文提出GraphBridge框架，实现GNN在不同任务与领域间的任意迁移，无需修改任务配置或图结构。GraphBridge通过为任意预训练GNN添加预测头与桥接网络，连接输入与输出层，既保留原模型内在知识，又支持任意维度输出。为缓解负迁移问题，该方法将源模型与同步训练的模型融合，降低在目标域上的源偏见。在包括Graph2Graph、Node2Node、Graph2Node和图到点云在内的多种迁移场景下进行了全面评估，基于16个代表性数据集的实证验证表明，该框架具备任务与领域无关的迁移学习能力，显著推进了GNN领域的进展。代码已公开于https://github.com/jujulili888/GraphBridge。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) are conventionally trained on a per-domain, per-task basis. It creates a significant barrier in transferring the acquired knowledge to different, heterogeneous data setups. This paper introduces GraphBridge, a novel framework to enable knowledge transfer across disparate tasks and domains in GNNs, circumventing the need for modifications to task configurations or graph structures. Specifically, GraphBridge allows for the augmentation of any pre-trained GNN with prediction heads and a bridging network that connects the input to the output layer. This architecture not only preserves the intrinsic knowledge of the original model but also supports outputs of arbitrary dimensions. To mitigate the negative transfer problem, GraphBridge merges the source model with a concurrently trained model, thereby reducing the source bias when applied to the target domain. Our method is thoroughly evaluated across diverse transfer learning scenarios, including Graph2Graph, Node2Node, Graph2Node, and graph2point-cloud. Empirical validation, conducted over 16 datasets representative of these scenarios, confirms the framework's capacity for task- and domain-agnostic transfer learning within graph-like data, marking a significant advancement in the field of GNNs. Code is available at https://github.com/jujulili888/GraphBridge.
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