dgl.nn (MXNet)

Conv Layers

GraphConv	Graph convolutional layer from Semi-Supervised Classification with Graph Convolutional Networks
RelGraphConv	Relational graph convolution layer from Modeling Relational Data with Graph Convolutional Networks
TAGConv	Topology Adaptive Graph Convolutional layer from Topology Adaptive Graph Convolutional Networks.
GATConv	Graph attention layer from Graph Attention Network
EdgeConv	EdgeConv layer from Dynamic Graph CNN for Learning on Point Clouds
SAGEConv	GraphSAGE layer from Inductive Representation Learning on Large Graphs
SGConv	SGC layer from Simplifying Graph Convolutional Networks
APPNPConv	Approximate Personalized Propagation of Neural Predictions layer from Predict then Propagate: Graph Neural Networks meet Personalized PageRank
GINConv	Graph Isomorphism layer from How Powerful are Graph Neural Networks?
GatedGraphConv	Gated Graph Convolution layer from Gated Graph Sequence Neural Networks
GMMConv	Gaussian Mixture Model Convolution layer from Geometric Deep Learning on Graphs and Manifolds using Mixture Model CNNs
ChebConv	Chebyshev Spectral Graph Convolution layer from Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
AGNNConv	Attention-based Graph Neural Network layer from Attention-based Graph Neural Network for Semi-Supervised Learning
NNConv	Graph Convolution layer from Neural Message Passing for Quantum Chemistry

Dense Conv Layers

DenseGraphConv	Graph Convolutional layer from Semi-Supervised Classification with Graph Convolutional Networks
DenseSAGEConv	GraphSAGE layer from Inductive Representation Learning on Large Graphs
DenseChebConv	Chebyshev Spectral Graph Convolution layer from Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Global Pooling Layers

SumPooling	Apply sum pooling over the nodes in the graph.
AvgPooling	Apply average pooling over the nodes in the graph.
MaxPooling	Apply max pooling over the nodes in the graph.
SortPooling	Pooling layer from An End-to-End Deep Learning Architecture for Graph Classification
GlobalAttentionPooling	Global Attention Pooling layer from Gated Graph Sequence Neural Networks
Set2Set	Set2Set operator from Order Matters: Sequence to sequence for sets

Heterogeneous Learning Modules

HeteroGraphConv

A generic module for computing convolution on heterogeneous graphs

Utility Modules

Sequential

A squential container for stacking graph neural network blocks