Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process is often inefficient in computation and memory usage. In this work, we propose a new optimization framework for semi-supervised learning on graphs. The proposed framework can be conveniently solved by the alternating optimization algorithms, resulting in significantly improved efficiency. Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.

翻译：图神经网络（GNNs）极大地推动了图上半监督节点分类任务的发展。现有的大多数GNNs采用端到端训练方式，可视为解决双层优化问题，但该过程在计算和内存使用上往往效率低下。本文提出了一种新的面向图上半监督学习的优化框架。该框架可通过交替优化算法便捷求解，从而显著提升效率。大量实验表明，所提方法在达到与最先进基线相当或更优性能的同时，具有显著更优的计算和内存效率。