Built on top of: F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal ...

Graph Neural Networks excel at modeling and analyzing complex and nonlinear ... and the second minimizing the squared difference between input and output of the autoencoder. Post-training, anomalies ...

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean ... Figure 8. Variational graph autoencoder (VGAE) in molecular graph generation. As the encoder of VGAE, the edge condition ...

In this paper, a novel local anomaly detection model DAGNN is proposed, which incorporates a graph neural network to better aggregate neighbors' distance information of each sample for forming its ...

Still, most of the current graph neural networks are based on supervised learning or semi ... To solve this problem, we propose a Deep Self-Supervised Attention Convolution Autoencoder Graph ...

Methods: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes ...

Console.WriteLine("Creating 9-6-9 tanh()" + " identity() neural network autoencoder "); NeuralNetwork nn = new NeuralNetwork(9, 6, 9, seed: 0); Console.WriteLine("Done "); The number of input nodes ...