AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange

TL;DR

This paper introduces AKE-GNN, a novel framework that enhances GNN performance by adaptively exchanging redundant and informative channels across multiple graph views, leading to improved results in various graph tasks.

Contribution

The paper proposes a new adaptive knowledge exchange strategy for GNNs that reactivates redundant channels by exchanging them with informative ones across multiple views.

Findings

Achieves superior performance on node, link, and graph classification tasks.

Consistently outperforms baseline GNN models and their ensembles.

Validated through extensive experiments on 15 datasets and 8 GNN models.

Abstract

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.