Graph Data

Graph data, representing relationships between entities, is increasingly important across diverse fields, driving research into efficient processing and analysis methods. Current research focuses on integrating graph data with large language models (LLMs) through techniques like graph flattening and alignment, as well as developing specialized graph neural networks (GNNs) tailored for tasks such as link prediction, node classification, and graph generation, often incorporating attention mechanisms and temporal dynamics. These advancements aim to improve scalability, accuracy, and interpretability in handling massive and complex graph datasets, impacting fields ranging from social network analysis to drug discovery and beyond.