4 papers
Are Large Language Models Suitable for Graph Computation? Progress and Prospects
Yuting Zhang, Yi Han, Kai Wang +3
Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algorithmic operations. Yet, it…
RadixGraph: A Fast, Space-Optimized Data Structure for Dynamic Graph Storage (Extended Version)
Haoxuan Xie, Junfeng Liu, Siqiang Luo +1
Dynamic graphs model many real-world applications, and as their sizes grow, efficiently storing and updating them becomes critical. We present RadixGraph, a fast and memory-efficie…
Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
Zulun Zhu, Tiancheng Huang, Kai Wang +3
Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucinati…
Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs
Kai Wang, Siqiang Luo, Caihua Shan +1
Inspired by the success of large language models, there is a trend toward developing graph foundation models to conduct diverse downstream tasks in various domains. However, curren…