Chapter2: Graph Representation Learning

Peng Cui, Tsinghua University,
Lingfei Wu, JD.COM Silicon Valley Research Center,
Jian Pei, Simon Fraser University,
Liang Zhao, Emory University,
Xiao Wang, Beijing University of Posts and Telecommunications,


Graph representation learning aims at assigning nodes in a graph to low-dimensional representations and effectively preserving the graph structure. Recently, a significant amount of progresses have been made toward this emerging graph analysis paradigm. In this chapter, we first summarize the motivation of graph representation learning. Afterwards and primarily, we provide a comprehensive overview of a large number of graph representation learning methods in a systematic manner, covering the traditional graph representation learning, modern graph representation learning, and graph neural networks..


  • Graph Representation Learning: An Introduction
  • Traditional Graph Embedding
  • Modern Graph Embedding
    • Structure-Property Perserving Graph Representation Learning
    • Graph Representation Learning with Side Information
    • Advanced Information Perserving Graph Representation Learning
  • Graph Neural Networks
  • Summary


author = "Cui, Peng and Wu, Lingfei and Pei, Jian and Zhao, Liang and Wang, Xiao",
editor = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Representation Learning",
booktitle = "Graph Neural Networks: Foundations, Frontiers, and Applications",
year = "2022",
publisher = "Springer Singapore",
address = "Singapore",
pages = "17--26",

P. Cui, L. Wu, J. Pei, L. Zhao, and X. Wang, “Graph representation learning,” in Graph Neural Networks: Foundations, Frontiers, and Applications, L. Wu, P. Cui, J. Pei, and L. Zhao, Eds. Singapore: Springer Singapore, 2022, pp. 17–26.