Chapter14: Graph Neural Networks: Graph Structure Learning

Yu Chen, Facebook AI,
Lingfei Wu, JD.COM Silicon Valley Research Center,


Due to the excellent expressive power of Graph Neural Networks (GNNs) on modeling graph-structure data, GNNs have achieved great success in various applications such as Natural Language Processing, Computer Vision, recommender systems, drug discovery and so on. However, the great success of GNNs relies on the quality and availability of graph-structured data which can either be noisy or unavailable. The problem of graph structure learning aims to discover useful graph structures from data, which can help solve the above issue. This chapter attempts to provide a comprehensive introduction of graph structure learning through the lens of both traditional machine learning and GNNs. After reading this chapter, readers will learn how this problem has been tackled from different perspectives, for different purposes, via different techniques, as well as its great potential when combined with GNNs. Readers will also learn promising future directions in this research area.


  • Introduction
  • Traditional Graph Structure Learning
    • Unsupervised Graph Structure Learning
    • Supervised Graph Structure Learning
  • Graph Structure Learning for Graph Neural Networks
    • Joint Graph Structure and Representation Learning
    • Connections to Other Problems
  • Future Directions
    • Robust Graph Structure Learning
    • Scalable Graph Structure Learning
    • Graph Structure Learning for Heterogeneous Graphs
  • Summary


author = "Chen, Yu and Wu, Lingfei",
editor = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Neural Networks: Graph Structure Learning",
booktitle = "Graph Neural Networks: Foundations, Frontiers, and Applications",
year = "2022",
publisher = "Springer Singapore",
address = "Singapore",
pages = "297--321",

Y. Chen and L. Wu, “Graph neural networks: Graph structure learning,” in Graph Neural Networks: Foundations, Frontiers, and Applications, L. Wu, P. Cui, J. Pei, and L. Zhao, Eds. Singapore: Springer Singapore, 2022, pp. 297–321.