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Faster distributed graph neural network training with GraphStorm v0.4
GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, ...
Tag: Graph
Graph Generative Pretrained Transformer (G2PT): An autoregressive model designed to learn graph structures by predicting the next token
Graph generation is an important task in several fields, including molecular design and social network analysis, due to its ability ...
Graph Structure Learning (GSLI) Framework: Advancing Spatio-Temporal Data Imputation via Multi-Scale Graph Learning
Spatio-temporal data Management involves the analysis of information collected in time and space, often through sensors. This data is crucial ...
This Amazon AI paper introduces DF-GNN: a dynamic kernel fusion framework for accelerating attention graph neural networks on GPUs
Graph neural networks (GNN) is a rapidly advancing field in machine learning, specifically designed to analyze graphically structured data representing ...
GRAF: A machine learning framework that converts multiplexed heterogeneous networks into homogeneous networks to make them more suitable for graph representation learning
Real-world networks, such as those in biomedical and multi-omics datasets, often exhibit complex structures characterized by multiple types of nodes ...
DeBaTeR: A new artificial intelligence method that leverages temporal information in collaborative neural graph filtering to improve both denoising and prediction performance.
Recommender systems have been widely applied to study user preferences; However, they face significant challenges in accurately capturing user preferences, ...
How to query a knowledge graph with LLM using gRAG
Google, Microsoft, LinkedIn and many more technology companies are using Graph RAG. Because? Let's understand it by building one from ...
Improving task scheduling in linguistic agents: Leveraging graph neural networks to improve task decomposition and decision making in large language models
Task planning in linguistic agents is gaining attention in LLM research, focusing on breaking down complex tasks into manageable subtasks ...
ConvKGYarn: Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models
The rapid evolution of large language models (LLMs) and conversational assistants requires dynamic, scalable, and configurable conversational datasets for training ...
Intel and Salesforce researchers propose SynthKG: a multi-step document-level ontology-free knowledge graph synthesis workflow based on LLM
Knowledge Graph (KG) synthesis is gaining ground in artificial intelligence research because it can build structured knowledge representations from expansive, ...
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