11 citations · 24 across the 4 of their papers we have counts for
6 papers
Optimizing open-domain question answering with graph-based retrieval augmented generation
Joyce Cahoon, Prerna Singh, Nick Litombe +6
In this work, we benchmark various graph-based retrieval-augmented generation (RAG) systems across a broad spectrum of query types, including OLTP-style (fact-based) and OLAP-style…
Principal Graph Encoder Embedding and Principal Community Detection
Cencheng Shen, Yuexiao Dong, Carey E. Priebe +3
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves ver…
Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
Cencheng Shen, Jonathan Larson, Ha Trinh +1
This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community re…
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Darren Edge, Ha Trinh, Newman Cheng +7
The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over pri…
Discovering Communication Pattern Shifts in Large-Scale Labeled Networks using Encoder Embedding and Vertex Dynamics
Cencheng Shen, Jonathan Larson, Ha Trinh +3
Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge in understanding social dynamics, detecting anomalies,…
Synergistic Graph Fusion via Encoder Embedding
Cencheng Shen, Carey E. Priebe, Jonathan Larson +1
In this paper, we introduce a method called graph fusion embedding, designed for multi-graph embedding with shared vertex sets. Under the framework of supervised learning, our meth…