tf_geometricTensorFlow 图神经网络框架

# coding=utf-8

import numpy as np

import tf_geometric as tfg

import tensorflow as tf



graph = tfg.Graph(

    x=np.random.randn(5, 20),  # 5个节点, 20维特征

    edge_index=[[0, 0, 1, 3],

                [1, 2, 2, 1]]  # 4个无向边

)



print("Graph Desc: \n", graph)



graph.convert_edge_to_directed()  # 预处理边数据，将无向边表示转换为有向边表示

print("Processed Graph Desc: \n", graph)

print("Processed Edge Index:\n", graph.edge_index)



# 多头图注意力网络（Multi-head GAT）

gat_layer = tfg.layers.GAT(units=4, num_heads=4, activation=tf.nn.relu)

output = gat_layer([graph.x, graph.edge_index])

print("Output of GAT: \n", output)

Graph Desc:

 Graph Shape: x => (5, 20)  edge_index => (2, 4)    y => None



Processed Graph Desc:

 Graph Shape: x => (5, 20)  edge_index => (2, 8)    y => None



Processed Edge Index:

 [[0 0 1 1 1 2 2 3]

 [1 2 0 2 3 0 1 1]]



Output of GAT:

 tf.Tensor(

[[0.22443159 0.         0.58263206 0.32468423]

 [0.29810357 0.         0.19403605 0.35630274]

 [0.18071976 0.         0.58263206 0.32468423]

 [0.36123228 0.         0.88897204 0.450244  ]

 [0.         0.         0.8013462  0.        ]], shape=(5, 4), dtype=float32)

• 安装
  
  
  
  • 环境要求与依赖库
  
  
  • 使用pip一键安装tf_geometric及依赖
  
  


• 快速入门
  
  
  
  • 使用简单示例快速入门
  
  
  • 面向对象接口（OOP API）和函数式接口（Functional API）
  
  

节点分类

• 图卷积网络 Graph Convolutional Network (GCN)


• 多头图注意力网络 Multi-head Graph Attention Network (GAT)


• Approximate Personalized Propagation of Neural Predictions (APPNP)


• Inductive Representation Learning on Large Graphs (GraphSAGE)


• 切比雪夫网络 Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (ChebyNet)


• Simple Graph Convolution (SGC)


• Topology Adaptive Graph Convolutional Network (TAGCN)


• Deep Graph Infomax (DGI)


• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification (DropEdge)


• 基于图卷积网络的文本分类 Graph Convolutional Networks for Text Classification (TextGCN)

图分类

• 平均池化 MeanPooling


• Graph Isomorphism Network (GIN)


• 自注意力图池化 Self-Attention Graph Pooling (SAGPooling)


• 可微池化 Hierarchical Graph Representation Learning with Differentiable Pooling (DiffPool)


• Order Matters: Sequence to Sequence for Sets (Set2Set)


• ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (ASAP)


• An End-to-End Deep Learning Architecture for Graph Classification (SortPool)


• 最小割池化 Spectral Clustering with Graph Neural Networks for Graph Pooling (MinCutPool)

链接预测

• 图自编码器 Graph Auto-Encoder (GAE)

保存和载入模型

• 模型保存和载入


• 使用tf.train.Checkpoint进行模型保存和载入

分布式训练

• 分布式图卷积网络（节点分类）


• 分布式平均池化（图分类）

@misc{hu2021efficient,

      title={Efficient Graph Deep Learning in TensorFlow with tf_geometric},

      author={Jun Hu and Shengsheng Qian and Quan Fang and Youze Wang and Quan Zhao and Huaiwen Zhang and Changsheng Xu},

      year={2021},

      eprint={2101.11552},

      archivePrefix={arXiv},

      primaryClass={cs.LG}

}