The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

Specifically, in this paper we introduce a graph neural network (GNN) designed to map the received pilot signals to optimized beamforming matrices and to model interactions among user equipment within ...

Persistent Link: https://ieeexplore.ieee.org/servlet/opac?punumber=34 ...

If you happen to use or modify this code, please remember to cite our paper: Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli: A Fair Comparison of Graph Neural Networks for Graph ...

A PyG re-implementation of NBFNet can be found here. You may install the dependencies via either conda or pip. Generally, NBFNet works with Python 3.7/3.8 and PyTorch ...

The interplay between graph analytics and large language models (LLMs) represents a promising frontier for advancing ...

Originally created by Meta, PyTorch has become an important tool for machine learning and people developing AI models ...

Department of Chemical Engineering and Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, P. R. China ...

The researchers from Erlangen have now presented a method aimed at considerably increasing the speed of analysis using deep neural networks. The first step involves transforming the time series ...

Deep neural networks will allow signal transfer of nerve ... series recordings into two-dimensional histograms. These compact graphs that can be compared to QR codes eliminate superfluous ...