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Detecting multiple communities using quantum annealing on the D-Wave system

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  • Christian F A Negre
  • Hayato Ushijima-Mwesigwa
  • Susan M Mniszewski

Abstract

A very important problem in combinatorial optimization is the partitioning of a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the “highest quality” possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.

Suggested Citation

  • Christian F A Negre & Hayato Ushijima-Mwesigwa & Susan M Mniszewski, 2020. "Detecting multiple communities using quantum annealing on the D-Wave system," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-14, February.
    • Handle: RePEc:plo:pone00:0227538
    DOI: 10.1371/journal.pone.0227538
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    References listed on IDEAS

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    1. Roger Guimerà & Luís A. Nunes Amaral, 2005. "Functional cartography of complex metabolic networks," Nature, Nature, vol. 433(7028), pages 895-900, February.
    2. Yu-Hsiang Fu & Chung-Yuan Huang & Chuen-Tsai Sun, 2017. "A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-30, November.
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    Cited by:

    1. Jonas Stein & Dominik Ott & Jonas Nüßlein & David Bucher & Mirco Schönfeld & Sebastian Feld, 2023. "NISQ-Ready Community Detection Based on Separation-Node Identification," Mathematics, MDPI, vol. 11(15), pages 1-19, July.

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