The GNN model consists of 2 GCN hidden layers. The model implements ADAM optimizer (gradient momentum, RMS prop) and the gradient is calculated automatically using pytorch autograd. The data is scaled ...

This project focuses on training a Graph Neural Network (GNN) model for the classification of MNIST images, based on the methodology described in the paper "Graph Neural Networks for Image ...

Abstract: This paper proposes a novel deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), termed model-based GNN.

In this study, we construct a dynamic inductive predictive graph neural network (DIP-GNN) model, which introduces a sequence decomposition algorithm to separate the wave modes of the data, which can ...

In this paper, we propose a model-agnostic framework Ada-GNN that provides personalized GNN models for specific sets of nodes. Intuitively, it is desirable that every node has its own model. But ...