Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are highly redundant, which inevitably limits the performance of GNNs. Instead of removing these redundant channels for efficiency consideration, we aim to reactivate them to enlarge the representation capacity of GNNs for effective graph learning. In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal. We introduce a novel GNN learning framework named AKE-GNN, which performs the Adaptive Knowledge Exchange strategy among multiple graph views generated by graph augmentations. AKE-GNN first trains multiple GNNs each corresponding to one graph view to obtain informative channels. Then, AKE-GNN iteratively exchanges redundant channels in the weight parameter matrix of one GNN with informative channels of another GNN in a layer-wise manner. Additionally, existing GNNs can be seamlessly incorporated into our framework. AKE-GNN achieves superior performance compared with various baselines across a suite of experiments on node classification, link prediction, and graph classification. In particular, we conduct a series of experiments on 15 public benchmark datasets, 8 popular GNN models, and 3 graph tasks and show that AKE-GNN consistently outperforms existing popular GNN models and even their ensembles. Extensive ablation studies and analyses on knowledge exchange methods validate the effectiveness of AKE-GNN.

翻译：图神经网络（GNN）已广泛应用于各类图挖掘任务。然而，近期研究表明，训练良好的GNN中学习到的权重（通道）存在高度冗余，这不可避免地限制了GNN的性能。我们并非出于效率考量移除这些冗余通道，而是旨在通过重新激活它们来扩展GNN的表示能力，从而实现高效图学习。本文提出用其他信息通道替代这些冗余通道以达成该目标。我们引入了一种名为AKE-GNN的新型GNN学习框架，该框架在图增强生成的多图视图间执行自适应知识交换策略。AKE-GNN首先训练多个GNN（每个对应一个图视图）以获取信息通道，随后通过层级方式，将某一GNN权重参数矩阵中的冗余通道与另一GNN的信息通道进行迭代交换。此外，现有GNN可无缝集成到我们的框架中。在节点分类、链接预测和图分类的一系列实验中，AKE-GNN相比各类基线方法展现出优越性能。特别地，我们在15个公开基准数据集、8种主流GNN模型和3种图任务上进行了系列实验，结果表明AKE-GNN始终优于现有主流GNN模型甚至其集成方法。关于知识交换方法的大量消融实验与分析进一步验证了AKE-GNN的有效性。