Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.

翻译：图表结构学习 (GSL) 旨在学习图形神经网络(GNN) 的匹配矩阵,显示在提高 GNNs 性能方面的巨大潜力。大多数现有的GSL 工作采用一个联合学习框架,即为下游任务优化估计的匹配矩阵和 GNN 参数。然而,由于GSL 基本上是一个链接的预测任务,其目标可能与下游任务的目标大不相同。这两个目标的不一致限制了GSL 学习潜在最佳图形结构的方法。此外,在估算和优化GNNNS 模型过程中,联合学习框架在时间和空间方面都存在可缩放问题。为了缓解这些问题,我们建议了一个图形结构改进框架,其中对相近的匹配矩阵矩阵矩阵矩阵和图中,通过精细的模型评估,对GNNUR 的精细的精度进行精细的精细的精度,对GGNN值进行精细的精细的精度调整。</s>