Text Attributed Graph

Text-attributed graphs (TAGs) combine graph structures with textual node and/or edge information, presenting a rich data representation for various applications. Current research focuses on leveraging large language models (LLMs) to enhance graph representation learning, often integrating LLMs with graph neural networks (GNNs) or employing LLMs for data augmentation and prompt-based learning. This interdisciplinary approach aims to improve the accuracy and efficiency of graph-based tasks like node classification and link prediction, impacting fields such as social network analysis, recommendation systems, and bioinformatics. The development of standardized datasets and benchmarks is also a key area of focus, facilitating more robust comparisons and advancements in the field.