4 papers
Towards Training and Inference of Graph Neural Networks in Graph Databases
Victor Pekkari, Harry Wik, Alfred Clemedtson +2
Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Graph databases provide scalab…
GDS Agent for Graph Algorithmic Reasoning
Borun Shi, Ioannis Panagiotas
Large language models (LLMs) have shown remarkable multimodal information processing and reasoning ability. When equipped with tools through function calling and enhanced with retr…
GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases
Alfred Clemedtson, Borun Shi
Large language models have shown remarkable language processing and reasoning ability but are prone to hallucinate when asked about private data. Retrieval-augmented generation (RA…
Graph Neural Networks on Graph Databases
Dmytro Lopushanskyy, Borun Shi
Training graph neural networks on large datasets has long been a challenge. Traditional approaches include efficiently representing the whole graph in-memory, designing parameter e…