Opportunities and challenges of graph neural networks in electrical engineering

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. Graph neural networks (GNNs) are an important technology for electrical engineering, physics, materials science and biology. This Review discusses how GNNs can help these research fields and how electrical engineering can resolve the technical challenges of GNNs.