DGL
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Get Started

  • Install DGL
  • DGL at a Glance
  • DGL Basics
  • PageRank with DGL Message Passing
  • Batched Graph Classification with DGL
  • Working with Heterogeneous Graphs in DGL

Features

  • Builtin message passing functions
  • NN Modules
    • NN Modules
      • NN Modules (PyTorch)
      • NN Modules (MXNet)
    • Contribution Guide

Model Tutorials

  • Model overview
  • Graph Neural Network and its variant
  • Dealing with many small graphs
  • Generative models
  • Old (new) wines in new bottle
  • Training on giant graphs

API Reference

  • API Reference

Developer Notes

  • Contribute to DGL
  • DGL Foreign Function Interface (FFI)

Misc

  • FAQ
  • Environment Variables
  • Resources
DGL
  • Docs »
  • NN Modules »
  • NN Modules
  • Edit on GitHub

NN ModulesΒΆ

Package for neural network common components.

  • NN Modules (PyTorch)
    • Conv Layers
      • GraphConv
      • RelGraphConv
      • TAGConv
      • GATConv
      • EdgeConv
      • SAGEConv
      • SGConv
      • APPNPConv
      • GINConv
      • GatedGraphConv
      • GMMConv
      • ChebConv
      • AGNNConv
      • NNConv
    • Dense Conv Layers
      • DenseGraphConv
      • DenseSAGEConv
      • DenseChebConv
    • Global Pooling Layers
      • SumPooling
      • AvgPooling
      • MaxPooling
      • SortPooling
      • GlobalAttentionPooling
      • Set2Set
      • SetTransformerEncoder
      • SetTransformerDecoder
    • Utility Modules
      • KNNGraph
      • SegmentedKNNGraph
      • Edge Softmax
  • NN Modules (MXNet)
    • Conv Layers
      • GraphConv
      • RelGraphConv
      • TAGConv
    • Global Pooling Layers
      • SumPooling
      • AvgPooling
      • MaxPooling
      • SortPooling
      • GlobalAttentionPooling
      • Set2Set
    • Utility Modules
      • Edge Softmax
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