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ogbn-papers100M
The ogbn-papers100M dataset is a benchmark for graph convolutional networks. -
ogbn-products
Graph Neural Networks (GNNs) have become a popular approach for various applications, ranging from social network analysis to modeling chemical properties of molecules. -
Auto-Differentiation of Relational Computations for Very Large Scale Machine ...
The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally. -
Simplifying graph convolutional networks
Simplifying graph convolutional networks. -
SoGCN: Second-Order Graph Convolutional Networks
Graph Convolutional Networks (GCNs) with multi-hop aggregation is more expressive than one-hop GCN but suffers from higher model complexity. Finding the shortest aggregation... -
Generalization bounds for graph convolutional neural networks via Rademacher ...
This paper aims at studying the sample complexity of graph convolutional neural networks (GCNs), by providing tight upper bounds of Rademacher complexity for GCN models with a... -
GRAPH CONVOLUTIONAL NETWORKS FROM THE PER-SPECTIVE OF SHEAVES AND THE NEURAL ...
Graph convolutional networks are a popular class of deep neural network algorithms which have shown success in a number of relational learning tasks. Despite their success,... -
Distance-Geometric Graph Convolutional Network (DG-GCN) for Three-Dimensional...
Distance-geometric graph representation for 3D graphs, utilizing continuous-filter convolutional layers with filter-generating networks. -
ESOL and FreeSolv
Experimental results of DG-GAT for the ESOL and FreeSolv datasets show major improvement over those of the standard graph convolution network based on 2D molecular graphs. -
Generic Framework for Convolution on Arbitrary Structures
The dataset used in the paper is a generic framework for convolution on arbitrary structures, which includes grid convolutions and graph convolutions. -
Spatio-Temporal Graph Convolutional Networks
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
