Graph Neural Architecture Search
Graph Neural Architecture Search (GNAS) automates the design of efficient and effective Graph Neural Networks (GNNs), aiming to overcome the limitations of manual design. Current research focuses on developing hardware-aware GNAS methods for resource-constrained environments like edge devices, exploring unsupervised search techniques to handle unlabeled data, and employing novel search strategies such as evolutionary algorithms and even leveraging large language models like GPT-4 to guide the process. These advancements are significant because they accelerate the development of GNNs tailored to specific hardware and data characteristics, leading to improved performance and wider applicability in various domains, including molecular property prediction and vision-based applications.