Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks.

翻译：基于消息传递范式，已有大量研究提出了多样且有效的特征传播机制以提升图神经网络（GNN）的性能。然而，作为消息传递框架另一核心操作的特征变换却鲜受关注。本文首先通过实验探究典型GNN中特征变换操作的性能表现。出乎意料的是，我们发现GNN并未完全释放其固有特征变换操作的能力。基于此观察，我们提出双向知识迁移（BiKT）——一种即插即用的方法，可在不修改原始架构的情况下释放特征变换操作的潜能。将特征变换操作视为与原始GNN共享参数的派生表示学习模型，该模型的直接预测能提供拓扑无关的知识反馈，进而指导GNN及其内部特征变换的学习。在此基础上，BiKT不仅允许我们从GNN及其派生模型中获取知识，还能通过将知识注入对方实现相互促进。此外，我们从领域自适应角度进行了理论分析，证明BiKT能够收紧GNN的泛化界。在多达7个数据集上与5种典型GNN配合的实验表明，BiKT可为原始GNN带来0.5%-4%的性能提升，获得增强型GNN。同时，派生模型也展现出与原始GNN相当甚至更优的性能，可灵活独立应用于其他特定下游任务。