  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.
