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}
}