Graph Neural Networks (GNNs) are widely used for analyzing graph-structured data. Most GNN methods are highly sensitive to the quality of graph structures and usually require a perfect graph structure for learning informative embeddings. However, the pervasiveness of noise in graphs necessitates learning robust representations for real-world problems. To improve the robustness of GNN models, many studies have been proposed around the central concept of Graph Structure Learning (GSL), which aims to jointly learn an optimized graph structure and corresponding representations. Towards this end, in the presented survey, we broadly review recent progress of GSL methods for learning robust representations. Specifically, we first formulate a general paradigm of GSL, and then review state-of-the-art methods classified by how they model graph structures, followed by applications that incorporate the idea of GSL in other graph tasks. Finally, we point out some issues in current studies and discuss future directions.

中文翻译：

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用于稳健表示的深度图结构学习：一项调查

图神经网络（GNN）被广泛用于分析图结构数据。大多数GNN方法对图结构的质量高度敏感，并且通常需要完美的图结构来学习信息性嵌入。但是，图中的噪声无处不在，因此有必要为现实世界中的问题学习可靠的表示形式。为了提高GNN模型的鲁棒性，围绕图结构学习（GSL）的中心概念提出了许多研究，旨在共同学习优化的图结构和相应的表示形式。为此，在提出的调查中，我们广泛回顾了GSL方法在学习鲁棒表示中的最新进展。具体来说，我们首先制定GSL的一般范式，然后回顾按模型结构建模方式分类的最新方法，其次是将GSL理念纳入其他图形任务的应用程序。最后，我们指出了当前研究中的一些问题，并讨论了未来的方向。