In this paper, we study the problem of learning the weights of a deep convolutional neural network. We consider a network where convolutions are carried out over non-overlapping patches. We develop an ...

Graph neural networks learn node embeddings by recursively sampling and aggregating nodes in a graph, while existing methods have a fixed pattern of node sampling and aggregation, and usually only ...

Classical graph neural network approaches such as Graph Convolutional Network (GCN) and GraphSage achieve superior performance not only because of the powerful neural network architectures employed, ...

Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. "Convolutional neural networks on graphs with fast localized spectral filtering." Advances in Neural Information Processing Systems. 2016 ...

2.3 Graph Convolutional Neural Network 2.3.1 Graph Convolution. In the graph theory, a graph is presented by the graph Laplacian L. It is computed by the degree matrix D minus the adjacency matrix A, ...

A Higher-Order Graph Convolutional Layer. Sami A Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Hrayr Harutyunyan. NeurIPS, 2018. [link] In traditional Convolutional Neural Networks ...

And since neural graph networks require modified convolution and pooling operators, many Python packages like PyTorch Geometric, StellarGraph, and DGL have emerged for working with graphs. In Keras ...

Lattice vibrational frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of ...