Graph Processing

Graph processing focuses on efficiently analyzing and manipulating data represented as graphs, aiming to extract meaningful insights and facilitate complex computations. Current research emphasizes developing and optimizing graph neural networks (GNNs), including variations for dynamic graphs and multimodal data integration, to improve performance and scalability across diverse applications. These advancements are crucial for tackling challenges in various fields, such as geospatial analysis, image processing (including hyperspectral imaging), and natural language processing through the integration of graph data with large language models. Hardware acceleration techniques, like those using silicon photonics, are also being explored to enhance the speed and energy efficiency of graph processing.