dgl.nn (PyTorch)

Conv Layers

GraphConv	Graph convolutional layer from Semi-Supervised Classification with Graph Convolutional Networks
EdgeWeightNorm	This module normalizes positive scalar edge weights on a graph following the form in GCN.
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
GATv2Conv	GATv2 from How Attentive are Graph Attention Networks?
EGATConv	Graph attention layer that handles edge features from Rossmann-Toolbox (see supplementary data)
EdgeGATConv	Graph attention layer with edge features from SCENE
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 Network layer from How Powerful are Graph Neural Networks?
GINEConv	Graph Isomorphism Network with Edge Features, introduced by Strategies for Pre-training 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
AtomicConv	Atomic Convolution Layer from Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
CFConv	CFConv from SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
DotGatConv	Apply dot product version of self attention in Graph Attention Network
TWIRLSConv	Convolution together with iteratively reweighting least squre from Graph Neural Networks Inspired by Classical Iterative Algorithms
TWIRLSUnfoldingAndAttention	Description
GCN2Conv	Graph Convolutional Network via Initial residual and Identity mapping (GCNII) from Simple and Deep Graph Convolutional Networks
HGTConv	Heterogeneous graph transformer convolution from Heterogeneous Graph Transformer
GroupRevRes	Grouped reversible residual connections for GNNs, as introduced in Training Graph Neural Networks with 1000 Layers
EGNNConv	Equivariant Graph Convolutional Layer from E(n) Equivariant Graph Neural Networks
PNAConv	Principal Neighbourhood Aggregation Layer from Principal Neighbourhood Aggregation for Graph Nets
DGNConv	Directional Graph Network Layer from Directional Graph Networks

CuGraph Conv Layers

CuGraphRelGraphConv	An accelerated relational graph convolution layer from Modeling Relational Data with Graph Convolutional Networks that leverages the highly-optimized aggregation primitives in cugraph-ops.
CuGraphGATConv	Graph attention layer from Graph Attention Networks, with the sparse aggregation accelerated by cugraph-ops.
CuGraphSAGEConv	An accelerated GraphSAGE layer from Inductive Representation Learning on Large Graphs that leverages the highly-optimized aggregation primitives in cugraph-ops:

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 a graph.
AvgPooling	Apply average pooling over the nodes in a graph.
MaxPooling	Apply max pooling over the nodes in a graph.
SortPooling	Sort Pooling from An End-to-End Deep Learning Architecture for Graph Classification
WeightAndSum	Compute importance weights for atoms and perform a weighted sum.
GlobalAttentionPooling	Global Attention Pooling from Gated Graph Sequence Neural Networks
Set2Set	Set2Set operator from Order Matters: Sequence to sequence for sets
SetTransformerEncoder	The Encoder module from Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
SetTransformerDecoder	The Decoder module from Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Score Modules for Link Prediction and Knowledge Graph Completion

EdgePredictor	Predictor/score function for pairs of node representations
TransE	Similarity measure from Translating Embeddings for Modeling Multi-relational Data
TransR	Similarity measure from Learning entity and relation embeddings for knowledge graph completion

Heterogeneous Learning Modules

HeteroGraphConv	A generic module for computing convolution on heterogeneous graphs.
HeteroLinear	Apply linear transformations on heterogeneous inputs.
HeteroEmbedding	Create a heterogeneous embedding table.
TypedLinear	Linear transformation according to types.

Utility Modules

Sequential	A sequential container for stacking graph neural network modules
WeightBasis	Basis decomposition from Modeling Relational Data with Graph Convolutional Networks
KNNGraph	Layer that transforms one point set into a graph, or a batch of point sets with the same number of points into a batched union of those graphs.
SegmentedKNNGraph	Layer that transforms one point set into a graph, or a batch of point sets with different number of points into a batched union of those graphs.
RadiusGraph	Layer that transforms one point set into a bidirected graph with neighbors within given distance.
JumpingKnowledge	The Jumping Knowledge aggregation module from Representation Learning on Graphs with Jumping Knowledge Networks
NodeEmbedding	Class for storing node embeddings.
GNNExplainer	GNNExplainer model from GNNExplainer: Generating Explanations for Graph Neural Networks
HeteroGNNExplainer	GNNExplainer model from GNNExplainer: Generating Explanations for Graph Neural Networks, adapted for heterogeneous graphs
SubgraphX	SubgraphX from On Explainability of Graph Neural Networks via Subgraph Explorations <https://arxiv.org/abs/2102.05152>
HeteroSubgraphX	SubgraphX from On Explainability of Graph Neural Networks via Subgraph Explorations, adapted for heterogeneous graphs
LabelPropagation	Label Propagation from Learning from Labeled and Unlabeled Data with Label Propagation

Network Embedding Modules

DeepWalk	DeepWalk module from DeepWalk: Online Learning of Social Representations
MetaPath2Vec	metapath2vec module from metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Utility Modules for Graph Transformer

DegreeEncoder	Degree Encoder, as introduced in Do Transformers Really Perform Bad for Graph Representation?
LapPosEncoder	Laplacian Positional Encoder (LPE), as introduced in GraphGPS: General Powerful Scalable Graph Transformers
PathEncoder	Path Encoder, as introduced in Edge Encoding of Do Transformers Really Perform Bad for Graph Representation?
SpatialEncoder	Spatial Encoder, as introduced in Do Transformers Really Perform Bad for Graph Representation?
SpatialEncoder3d	3D Spatial Encoder, as introduced in One Transformer Can Understand Both 2D & 3D Molecular Data
BiasedMHA	Dense Multi-Head Attention Module with Graph Attention Bias.
GraphormerLayer	Graphormer Layer with Dense Multi-Head Attention, as introduced in Do Transformers Really Perform Bad for Graph Representation?
EGTLayer	EGTLayer for Edge-augmented Graph Transformer (EGT), as introduced in `Global Self-Attention as a Replacement for Graph Convolution Reference `<https://arxiv.org/pdf/2108.03348.pdf>`_