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Quantum-inspired measures of network distinguishability

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Abstract

The recent emergence of information physics as a theoretical foundation for complex networks has inspired the utilization of measures, initially developed for use with quantum mechanical systems, for the solution of graph theory research problems. Network comparison is one such research problem that arises often in all domains, when entities that interact with each other possibly with more than one discrete interaction types are studied. A network similarity measure is required for any data mining application on graphs, such as graph clustering, classification, or outlier detection. A natural starting point for the identification of such a network similarity measure is information physics, offering a series of measures typically used to quantify the distance of quantum states. These quantum-inspired methods satisfy the mathematical requirements for graph similarity while offering high interpretability. In this work, we characterize these measures for use with monoplex and multiplex networks on experiments with synthetic data, and we report results on real-world applications to compare with a series of state-of-the-art and well-established methods of graph distinguishability.

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Acknowledgements

This research is supported in part by the U.S. Army Research Laboratory subaward 555080-78055 under Prime Contract No.W911NF2220001, the U.S. Army Corp of Engineers Engineer Research and Development Center under Cooperative Agreement W9132V-22-2-0001, and Temple University office of the Vice President for Research 2022 Catalytic Collaborative Research Initiative Program AI & ML Focus Area.

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Authors and Affiliations

  1. Computer and Information Sciences Department, Temple University, 1925 N. 12th Street, Philadelphia, PA, 19122, USA

    Athanasia Polychronopoulou, Jumanah Alshehri & Zoran Obradovic

  2. Center for Data Analytics and Biomedical Informatics, Temple University, 1925 N. 12th Street, Philadelphia, PA, 19122, USA

    Zoran Obradovic

Authors
  1. Athanasia Polychronopoulou
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  2. Jumanah Alshehri
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  3. Zoran Obradovic
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Contributions

AP conceived the presented study, performed the design and implementation of the research, and the analysis of the results. JA processed data and prepared tables. Both AP and ZO contributed to the final version of the manuscript. ZO supervised the project.

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Correspondence to Athanasia Polychronopoulou.

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Polychronopoulou, A., Alshehri, J. & Obradovic, Z. Quantum-inspired measures of network distinguishability. Soc. Netw. Anal. Min. 13, 69 (2023). https://doi.org/10.1007/s13278-023-01069-w

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