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On Graph Learning with Neural Networks

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

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Abstract

Graphs are ideal for modeling natural systems where relations may be intrinsic among data objects. With massive data available, learning graph models from data has become potentially feasible as well as necessary. Yet from the traditional machine learning perspective, learning structural topology of an unknown graphical model remains challenging. In particular, it is computationally intractable to learn graph topologies beyond a tree structure. Nevertheless, deep learning with neural networks, showing great potentials in visual imagery and other application domains, offers an alternative venue for effective machine learning on graphs. In this review, we discuss graph (structure) learning with deep neural networks. In particular, we examine graph neural networks (GNNs) from the task-based and the architecture-based perspectives, respectively.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Georgia, Athens, GA, 30602, USA

    Zahra Jandaghi & Liming Cai

Authors
  1. Zahra Jandaghi
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  2. Liming Cai
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Corresponding author

Correspondence to Zahra Jandaghi .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Jandaghi, Z., Cai, L. (2020). On Graph Learning with Neural Networks. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_43

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

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