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Graph Representation Learning and Its Applications: A Survey.

Van Thuy Hoang1, Hyeon-Ju Jeon2, Eun-Soon You1

  • 1Department of Artificial Intelligence, The Catholic University of Korea, 43, Jibong-ro, Bucheon-si 14662, Gyeonggi-do, Republic of Korea.

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Summary
This summary is machine-generated.

This paper overviews graph representation learning models, covering traditional and deep learning approaches. It explores various graph embedding techniques and their real-world applications.

Keywords:
graph embeddinggraph neural networksgraph representation learninggraph transformer

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Graphs are crucial for modeling real-world relational data.
  • Graph representation learning maps entities to vectors, preserving structure and relationships.
  • Numerous models have been developed over decades for graph representation learning.

Purpose of the Study:

  • To provide a comprehensive overview of graph representation learning models.
  • To cover traditional, state-of-the-art, and emerging techniques across different geometric spaces.
  • To discuss practical applications, challenges, and future research directions.

Main Methods:

  • Categorization of graph embedding models into five types: graph kernels, matrix factorization, shallow, deep-learning, and non-Euclidean models.
  • Inclusion of graph transformer and Gaussian embedding models.
  • Discussion of practical applications and domain-specific graph construction.

Main Results:

  • A structured overview of diverse graph embedding models is presented.
  • Key applications of graph embedding models are highlighted.
  • Current challenges and future research avenues are detailed.

Conclusions:

  • The paper offers a structured survey of graph embedding models.
  • It emphasizes the breadth of techniques and applications in graph representation learning.
  • It identifies open challenges and future research directions in the field.