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 ...

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

Abstract: Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have ...

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 ...

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 textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

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 ...