# tf_geometric Documentation¶

(中文版)

Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x.

Inspired by rusty1s/pytorch_geometric, we build a GNN library for TensorFlow. tf_geometric provides both OOP and Functional API, with which you can make some cool things.

## Efficient and Friendly API¶

We use Message Passing mechanism to implement graph neural networks, which is way efficient than the dense matrix based implementations and more friendly than the sparse matrix based ones. In addition, we provide easy and elegant APIs for complex GNN operations. The following example constructs a graph and applies a Multi-head Graph Attention Network (GAT) on it:

# 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 nodes, 20 features,
edge_index=[[0, 0, 1, 3],
[1, 2, 2, 1]]  # 4 undirected edges
)

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

graph.convert_edge_to_directed()  # pre-process edges
print("Processed Graph Desc: \n", graph)
print("Processed Edge Index:\n", graph.edge_index)

# Multi-head Graph Attention Network (GAT)
output = gat_layer([graph.x, graph.edge_index])
print("Output of GAT: \n", output)


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)