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Opportunities and challenges of graph neural networks in electrical engineering

Nature Reviews Electrical Engineering volume 1, pages 529–546 (2024)Cite this article

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Abstract

Graph neural networks (GNNs) are a class of deep learning algorithms that learn from graphs, networks and relational data. They have found applications throughout the sciences and made significant strides in electrical engineering. GNNs can learn from various electrical and electronic systems, such as electronic circuits, wireless networks and power systems, and assist in solving optimization or inference tasks where traditional approaches may be slow or inaccurate. Robust learning algorithms and efficient computational hardware developed and tailored for GNNs have amplified their relevance to electrical engineering. We have entered an era in which the studies of GNNs and electrical engineering are intertwined, opening to opportunities and challenges to researchers in both fields. This Review explores applications of GNNs that might generate notable impacts on electrical engineering. We discuss how GNNs are used to address electrical automatic design, as well as the modelling and management of wireless communication networks. Additionally, we delve into GNNs for high-energy physics, materials science and biology. Presenting the applications, data and computational challenges, the need for innovative algorithms and hardware solutions becomes clear.

Key points

  • Graph neural networks (GNNs) hold immense potential for harnessing data power to effectively tackle a range of application challenges in electrical engineering.

  • Research in information science and cutting-edge hardware within electrical engineering offer valuable insights into the practical implementation of GNNs, overcoming their limitations related to model reliability and computational efficiency.

  • GNNs find extensive applications in various scientific domains, including physics, materials science and biology. Electrical engineering methodologies can enhance GNN performance in these fields and, potentially, lead to significant impacts in science.

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Fig. 1: Application of graph neural networks in electronic design automation.
Fig. 2: Graph modelling and performance evaluations of graph neural network-enhanced resource allocation in wireless multi-hop networks.
Fig. 3: Visual representation of a collision event at the Large Hadron Collider and several graph neural network tasks.
Fig. 4: Molecular and crystal graphs and a chronological overview of graph neural networks used in material design.
Fig. 5: The ubiquity of networks (or graphs) in biological systems.

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Authors and Affiliations

  1. Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Eli Chien, Mufei Li, Cong Hao & Pan Li

  2. Physics, University of California San Diego, San Diego, CA, USA

    Anthony Aportela & Javier Duarte

  3. Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Kerr Ding, Shuyi Jia, Victor Fung & Yunan Luo

  4. Electrical and Computer Engineering, University of Texas Austin, Austin, TX, USA

    Supriyo Maji & David Pan

  5. Electrical and Computer Engineering, Rice University, Houston, TX, USA

    Zhongyuan Zhao & Santiago Segarra

  6. Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA

    Olgica Milenkovic

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E.C., M.L., A.A., K.D., S.J., S.M., Z.Z., J.D., V.F., Y.L., D.P., S.S. and P.L. researched data for the article. All authors contributed to the discussion of the content. E.C., M.L., A.A., K.D., S.J., S.M., Z.Z., C.H., O.M. and P.L. wrote the article. E.C., M.L., A.A., K.D., S.J., S.M., Z.Z., J.D., V.F., Y.L., D.P., S.S. and P.L. reviewed and edited the article.

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Chien, E., Li, M., Aportela, A. et al. Opportunities and challenges of graph neural networks in electrical engineering. Nat Rev Electr Eng 1, 529–546 (2024). https://doi.org/10.1038/s44287-024-00076-z

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