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Neural Network Optimization Based on Complex Network Theory: A Survey

Author

Listed:
  • Daewon Chung

    (Division of Electronics & Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea)

  • Insoo Sohn

    (Division of Electronics & Electrical Engineering, Dongguk University, Seoul 04620, Republic of Korea)

Abstract

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.

Suggested Citation

  • Daewon Chung & Insoo Sohn, 2023. "Neural Network Optimization Based on Complex Network Theory: A Survey," Mathematics, MDPI, vol. 11(2), pages 1-12, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:321-:d:1028432
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    References listed on IDEAS

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    1. Perotti, Juan I. & Tamarit, Francisco A. & Cannas, Sergio A., 2006. "A scale-free neural network for modelling neurogenesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(1), pages 71-75.
    2. Jon M. Kleinberg, 2000. "Navigation in a small world," Nature, Nature, vol. 406(6798), pages 845-845, August.
    3. Jinnuo Zhu & S. B. Goyal & Chaman Verma & Maria Simona Raboaca & Traian Candin Mihaltan, 2022. "Machine Learning Human Behavior Detection Mechanism Based on Python Architecture," Mathematics, MDPI, vol. 10(17), pages 1-31, September.
    4. Xiaohu Li & Feng Xu & Jinhua Zhang & Sunan Wang, 2013. "A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-8, June.
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