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Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks

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  • Neiva, Mariane B.
  • Bruno, Odemir M.

Abstract

The use of complex networks as a modern approach to understanding the world and its dynamics is well-established in the literature. The adjacency matrix, which provides a one-to-one representation of a complex network, can also yield several metrics of the graph. However, it is not always clear whether this representation is unique, as the permutation of rows and columns in the matrix can represent the same graph. To address this issue, the proposed methodology employs a sorting algorithm to rearrange the elements of the adjacency matrix of a complex graph in a specific order. The resulting sorted adjacency matrix is then used as input for feature extraction and machine learning algorithms to classify the networks. The results indicate that the proposed methodology outperforms previous literature results on synthetic and real-world data.

Suggested Citation

  • Neiva, Mariane B. & Bruno, Odemir M., 2023. "Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    • Handle: RePEc:eee:phsmap:v:626:y:2023:i:c:s0378437123006416
    DOI: 10.1016/j.physa.2023.129086
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    References listed on IDEAS

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    1. Dorogovtsev, S. N. & Mendes, J.F.F., 2013. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780199686711.
    2. Jeaneth Machicao & Edilson A Corrêa Jr. & Gisele H B Miranda & Diego R Amancio & Odemir M Bruno, 2018. "Authorship attribution based on Life-Like Network Automata," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-21, March.
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