  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.
