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Introduction

  • Paddle Graph Learning (PGL)

Quick Start

  • Quick Start Instructions
  • Quick Start with Heterogenous Graph

Examples

  • GCN: Graph Convolutional Networks
  • GAT: Graph Attention Networks
  • Using StaticGraphWrapper for Speed Optimization
  • node2vec: Scalable Feature Learning for Networks
  • GraphSAGE: Inductive Representation Learning on Large Graphs
  • DGI: Deep Graph Infomax
  • Distributed Deepwalk in PGL
  • Distribute GraphSAGE in PGL
  • GES: Graph Embedding with Side Information
  • LINE: Large-scale Information Network Embedding
  • SGC: Simplifying Graph Convolutional Networks
  • struc2vec: Learning Node Representations from Structural Identity
  • GATNE: General Attributed Multiplex HeTerogeneous Network Embedding
  • metapath2vec: Scalable Representation Learning for Heterogeneous Networks
  • Unsupervised GraphSAGE in PGL

API Reference

  • API Reference
    • pgl.graph module: Graph Storage
    • pgl.graph_wrapper module: Graph data holders for Paddle GNN.
    • pgl.layers: Predefined graph neural networks layers.
    • pgl.data_loader module: Some benchmark datasets.
    • pgl.utils.paddle_helper module: Some helper function for Paddle.
    • pgl.utils.mp_reader module: MultiProcessing reader helper function for Paddle.
    • pgl.heter_graph module: Heterogenous Graph Storage
    • pgl.heter_graph_wrapper module: Heterogenous Graph data holders for Paddle GNN.

The Team

  • The Team
pgl
  • Docs »
  • API Reference
  • Edit on GitHub

API ReferenceΒΆ

  • pgl.graph module: Graph Storage
  • pgl.graph_wrapper module: Graph data holders for Paddle GNN.
  • pgl.layers: Predefined graph neural networks layers.
  • pgl.data_loader module: Some benchmark datasets.
  • pgl.utils.paddle_helper module: Some helper function for Paddle.
  • pgl.utils.mp_reader module: MultiProcessing reader helper function for Paddle.
  • pgl.heter_graph module: Heterogenous Graph Storage
  • pgl.heter_graph_wrapper module: Heterogenous Graph data holders for Paddle GNN.
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