5 papers
GRAG: Graph Retrieval-Augmented Generation
Yuntong Hu, Zhihan Lei, Zheng Zhang +3
Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in…
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
Bo Pan, Zhen Xiong, Guanchen Wu +3
Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despi…
Taxonomy Tree Generation from Citation Graph
Yuntong Hu, Zhuofeng Li, Zheng Zhang +4
Constructing taxonomies from citation graphs is essential for organizing scientific knowledge, facilitating literature reviews, and identifying emerging research trends. However, m…
CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs
Yuntong Hu, Zhihan Lei, Zhongjie Dai +4
Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often s…
TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
Zhuofeng Li, Zixing Gou, Xiangnan Zhang +6
Text-Attributed Graphs (TAGs) augment graph structures with natural language descriptions, facilitating detailed depictions of data and their interconnections across various real-w…