dgl.nn (TensorFlow)

Conv Layers

GraphConv	Graph convolution from Semi-Supervised Classification with Graph Convolutional Networks
RelGraphConv	Relational graph convolution layer from Modeling Relational Data with Graph Convolutional Networks
GATConv	Graph Attention Layer from Graph Attention Network
SAGEConv	GraphSAGE layer from Inductive Representation Learning on Large Graphs
ChebConv	Chebyshev Spectral Graph Convolution layer from Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
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 Network layer from How Powerful are Graph Neural Networks?

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	Sort Pooling from An End-to-End Deep Learning Architecture for Graph Classification
GlobalAttentionPooling	Global Attention Pooling from Gated Graph Sequence Neural Networks

Heterogeneous Learning Modules