  Graphs are widely used to describe real-world objects and their interactions.
Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured
data, are highly sensitive to the quality of the given graph structures.
Therefore, noisy or incomplete graphs often lead to unsatisfactory
representations and prevent us from fully understanding the mechanism
underlying the system. In pursuit of an optimal graph structure for downstream
tasks, recent studies have sparked an effort around the central theme of Graph
Structure Learning (GSL), which aims to jointly learn an optimized graph
structure and corresponding graph representations. In the presented survey, we
broadly review recent progress in GSL methods. Specifically, we first formulate
a general pipeline of GSL and review state-of-the-art methods classified by the
way of modeling graph structures, followed by applications of GSL across
domains. Finally, we point out some issues in current studies and discuss
future directions.
