tf_geometric文档¶

tf_geometric是一个高效且友好的图神经网络库，同时支持TensorFlow 1.x 和 2.x。

受到rusty1s/pytorch_geometric项目的启发，我们为TensorFlow构建了一个图神经网络（GNN）库。 tf_geometric 同时提供面向对象接口（OOP API）和函数式接口（Functional API），你可以用它们来构建有趣的模型。

Github主页: https://github.com/CrawlScript/tf_geometric
开发文档: https://tf-geometric.readthedocs.io
论文: Efficient Graph Deep Learning in TensorFlow with tf_geometric

高效且友好的API¶

tf_geometric使用消息传递机制来实现图神经网络：相比于基于稠密矩阵的实现，它具有更高的效率；相比于基于稀疏矩阵的实现，它具有更友好的API。除此之外，tf_geometric还为复杂的图神经网络操作提供了简易优雅的API。下面的示例展现了使用tf_geometric构建一个图结构的数据，并使用多头图注意力网络（Multi-head GAT）对图数据进行处理的流程：

# 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)

输出:

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)

入门教程¶

教程列表¶

使用示例进行快速入门¶

强烈建议您通过下面的示例代码来快速入门tf_geometric：

节点分类¶

图分类¶

链接预测¶

图自编码器 Graph Auto-Encoder (GAE)

保存和载入模型¶

分布式训练¶

稀疏¶

稀疏节点特征

引用¶

如果您在科研出版物中使用了tf_geometric，欢迎引用下方的论文：

@inproceedings{DBLP:conf/mm/HuQFWZZX21,
  author    = {Jun Hu and
               Shengsheng Qian and
               Quan Fang and
               Youze Wang and
               Quan Zhao and
               Huaiwen Zhang and
               Changsheng Xu},
  editor    = {Heng Tao Shen and
               Yueting Zhuang and
               John R. Smith and
               Yang Yang and
               Pablo Cesar and
               Florian Metze and
               Balakrishnan Prabhakaran},
  title     = {Efficient Graph Deep Learning in TensorFlow with tf{\_}geometric},
  booktitle = {{MM} '21: {ACM} Multimedia Conference, Virtual Event, China, October
               20 - 24, 2021},
  pages     = {3775--3778},
  publisher = {{ACM}},
  year      = {2021},
  url       = {https://doi.org/10.1145/3474085.3478322},
  doi       = {10.1145/3474085.3478322},
  timestamp = {Wed, 20 Oct 2021 12:40:01 +0200},
  biburl    = {https://dblp.org/rec/conf/mm/HuQFWZZX21.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}