Abstract: This paper investigates a GraphRAG framework that integrates knowledge graphs into the Retrieval-Augmented Generation (RAG) architecture to enhance networking applications. While RAG has ...
Abstract: Performing training and inference for Graph Neural Networks (GNNs) under tight latency constraints has become increasingly difficult as real-world input graphs continue to grow. Compared to ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback