3D Graph

3D graphs represent complex relationships between three-dimensional data points, enabling analysis and modeling of diverse systems. Current research focuses on developing efficient algorithms for constructing and traversing these graphs, particularly for real-time applications like autonomous navigation and disease risk analysis, often employing hierarchical structures and graph neural networks (GNNs) to manage complexity. These advancements are significantly impacting fields ranging from robotics and urban planning to drug discovery and medical imaging, providing powerful tools for modeling and understanding intricate spatial relationships. The development of improved graph isomorphism testing methods and more complete 3D information incorporation within graph neural networks are also active areas of investigation.