Graph Task

Graph task research focuses on leveraging the power of large language models (LLMs) and graph neural networks (GNNs) to effectively process and reason with graph-structured data for various tasks, such as node classification, graph classification, and link prediction. Current research emphasizes improving LLMs' ability to handle complex graph structures by addressing limitations like "lost-in-distance" effects and positional biases in processing graph descriptions, often employing techniques like retrieval-augmented generation and multi-task prompting. These advancements are significant because efficient and accurate graph processing is crucial for numerous applications across diverse fields, including drug discovery, social network analysis, and knowledge representation.