GitHub

SEAL_OGB -- An Implementation of SEAL for OGB Link Prediction Tasks

This repository supports the following paper: M. Zhang, P. Li, Y. Xia, K. Wang, and L. Jin, Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. [PDF] SEAL ...
Frontiers

Modeling functional connectivity changes during an auditory language task using line graph neural networks

Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and ...
unite

Power of Graph RAG: The Future of Intelligent Search

As the world becomes increasingly data-driven, the demand for accurate and efficient search technologies has never been higher. Traditional search engines, while powerful, often struggle to meet the ...
eLife

Early stage NSCLS patients’ prognostic prediction with multi-information using transformer and graph neural network model

The study included 1705 patients with lung cancer (stages I and II), and a public data set for external validation (n=127). We proposed a graph with edges representing non-imaging patient ...
Frontiers

Extracting default mode network based on graph neural network for resting state fMRI study

Functional magnetic resonance imaging (fMRI)-based study of functional connections in the brain has been highlighted by numerous human and animal studies recently, which have provided significant ...
GitHub

microsoft/tf-gnn-samples

This repository is the code release corresponding to an article introducing graph neural networks (GNNs) with feature-wise linear modulation (Brockschmidt, 2019). In the paper, a number of GNN ...