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Found 11 papers, 3 papers with code
When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
GraphRAG-Bench features a comprehensive dataset with tasks of increasing difficulty, coveringfact retrieval, complex reasoning, contextual summarization, and creative generation, and a systematic evaluation across the entire pipeline, from graph constructionand knowledge retrieval to final generation.
A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise.
Automated Heterogeneous Network learning with Non-Recursive Message Passing
Instead of recursive homogeneous message passing, we introduce a non-recursive message passing mechanism for GNN to mitigate noise from uncorrelated node types in HINs.
Graph Cross-Correlated Network for Recommendation
Encoding all subgraph information into single vectors and inferring user-item relations with dot products can weaken the semantic information between user and item subgraphs, thus leaving untapped potential.
Neuro-Symbolic Entity Alignment via Variational Inference
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs.
Entity Alignment with Noisy Annotations from Large Language Models
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs.
Structure Guided Large Language Model for SQL Generation
Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL.
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages.
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
To this end, we propose Randomized Sparse Computation, which for the first time demonstrate the potential of training GNNs with approximated operations.
Theory and Applications of Financial Chaos Index
We develop a new stock market index that captures the chaos existing in the market by measuring the mutual changes of asset prices.
Lehmer Transform and its Theoretical Properties
We propose a new class of transforms that we call {\it Lehmer Transform} which is motivated by the {\it Lehmer mean function}.
