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International Conference on Complex Networks and their Applications

COMPLEX NETWORKS 2018: Complex Networks and Their Applications VII pp 182–193Cite as

Quantum Walk Neural Networks for Graph-Structured Data

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 813))

Abstract

In recent years, neural network architectures designed to operate on graph-structured data have pushed the state-of-the-art in the field. A large set of these architectures utilize a form of classical random walks to diffuse information throughout the graph. We propose quantum walk neural networks (QWNN), a novel graph neural network architecture based on quantum random walks, the quantum parallel to classical random walks. A QWNN learns a quantum walk on a graph to construct a diffusion operator which can then be applied to graph-structured data. We demonstrate the use of this model on a variety of prediction tasks on graphs involving temperature, biological, and molecular datasets.

Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053 (the ARL Network Science CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. This document does not contain technology or technical data controlled under either the U.S. International Traffic in Arms Regulations or the U.S. Export Administration Regulations.

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Author information

Authors and Affiliations

  1. University of Massachusetts Amherst, Amherst, MA, 01003, USA

    Stefan Dernbach, Arman Mohseni-Kabir & Don Towsley

  2. Raytheon BBN Technologies, Cambridge, MA, 02138, USA

    Siddharth Pal

Authors
  1. Stefan Dernbach
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  2. Arman Mohseni-Kabir
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  3. Siddharth Pal
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  4. Don Towsley
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Corresponding author

Correspondence to Stefan Dernbach .

Editor information

Editors and Affiliations

  1. Nokia Bell Labs, Cambridge, UK

    Luca Maria Aiello

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I UMR CNRS 6306 9, University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  4. Mathematical Institute, University of Oxford, Oxford, UK

    Renaud Lambiotte

  5. Department of Computer Science and Technology, The Computer Laboratory, University of Cambridge, Cambridge, UK

    Pietro Lió

  6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis M. Rocha

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Dernbach, S., Mohseni-Kabir, A., Pal, S., Towsley, D. (2019). Quantum Walk Neural Networks for Graph-Structured Data. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_15

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