Graph Embedding

Palash Goyal ()
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Palash Goyal: University of Southern California

A chapter in Machine Learning for Data Science Handbook, 2023, pp 339-351 from Springer

Abstract: Abstract Learning low-dimensional representation of nodes in a graph, a.k.a., graph embedding, has been increasingly becoming popular in solving everyday graph analysis tasks which require capturing various properties of the graphs. In this chapter, we explain the problem of graph embedding and describe several categories of approaches in this field. We cover three categories of embedding problems: (i) static graphs, (ii) dynamic graphs, and (iii) attributed graphs. The goal is to provide an understanding of the state-of-the-art methods in this area along with its wide applications.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_15

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DOI: 10.1007/978-3-031-24628-9_15

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