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A gentle introduction to deep learning for graphs.

Davide Bacciu1, Federico Errica1, Alessio Micheli1

  • 1Department of Computer Science, University of Pisa, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|June 20, 2020
PubMed
Summary
This summary is machine-generated.

This tutorial introduces deep learning for graphs, explaining core concepts and architectural elements for processing graph data. It provides a foundational understanding of graph representation learning and its applications.

Keywords:
Deep learning for graphsGraph neural networksLearning for structured data

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Graph data processing is a long-standing research area gaining traction in deep learning.
  • Recent research has expanded rapidly, but with limited systematization and attention to prior literature.
  • This work addresses the need for a structured introduction to deep learning for graphs.

Purpose of the Study:

  • To provide a tutorial introduction to deep learning for graphs.
  • To present a generalized formulation of graph representation learning.
  • To introduce fundamental building blocks for designing neural graph models.

Main Methods:

  • A top-down approach to graph representation learning.
  • A generalized formulation based on local and iterative processing of structured information.
  • Introduction of core architectural components for graph neural networks.

Main Results:

  • A consistent and progressive presentation of key concepts in deep learning for graphs.
  • A generalized framework for graph representation learning.
  • Identification of building blocks for novel graph neural network models.

Conclusions:

  • The paper offers a foundational understanding of deep learning for graphs.
  • It equips readers with the knowledge to design and understand graph neural network architectures.
  • It highlights research challenges and applications in the field of graph representation learning.