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Representation Learning on Graphs

  • Krishna Raj P. M.Email author
  • Ankith Mohan
  • K. G. Srinivasa
Chapter
Part of the Computer Communications and Networks book series (CCN)

Abstract

The primary challenge of applying machine learning in graph theory is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. In this chapter, we will look at a review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph convolutional networks. We will also look at methods to embed individual nodes as well as approaches to embed entire (sub)graphs.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Krishna Raj P. M.
    • 1
    Email author
  • Ankith Mohan
    • 1
  • K. G. Srinivasa
    • 2
  1. 1.Department of ISERamaiah Institute of TechnologyBangaloreIndia
  2. 2.Department of Information TechnologyC.B.P. Government Engineering CollegeJaffarpurIndia

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