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

## Project description

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.

## Homepage and Documentation

- Homepage: https://github.com/CrawlScript/tf_geometric
- Documentation: https://tf-geometric.readthedocs.io (中文版)
- Paper: Efficient Graph Deep Learning in TensorFlow with tf_geometric

## Efficient and Friendly

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) 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)

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)

## DEMO

We recommend you to get started with some demo.

### Node Classification

- Graph Convolutional Network (GCN)
- Multi-head Graph Attention Network (GAT)
- Approximate Personalized Propagation of Neural Predictions (APPNP)
- Inductive Representation Learning on Large Graphs (GraphSAGE)
- 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)
- Graph Convolutional Networks for Text Classification (TextGCN)

### Graph Classification

- MeanPooling
- Graph Isomorphism Network (GIN)
- Self-Attention Graph Pooling (SAGPooling)
- 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)
- Spectral Clustering with Graph Neural Networks for Graph Pooling (MinCutPool)

### Link Prediction

### Save and Load Models

### Distributed Training

## Installation

Requirements:

- Operation System: Windows / Linux / Mac OS
- Python: version >= 3.5
- Python Packages:
- tensorflow/tensorflow-gpu: >= 1.14.0 or >= 2.0.0b1
- numpy >= 1.17.4
- networkx >= 2.1
- scipy >= 1.1.0

Use one of the following commands below:

pip install -U tf_geometric # this will not install the tensorflow/tensorflow-gpu package pip install -U tf_geometric[tf1-cpu] # this will install TensorFlow 1.x CPU version pip install -U tf_geometric[tf1-gpu] # this will install TensorFlow 1.x GPU version pip install -U tf_geometric[tf2-cpu] # this will install TensorFlow 2.x CPU version pip install -U tf_geometric[tf2-gpu] # this will install TensorFlow 2.x GPU version

## OOP and Functional API

We provide both OOP and Functional API, with which you can make some cool things.

# coding=utf-8 import os # Enable GPU 0 os.environ["CUDA_VISIBLE_DEVICES"] = "0" import tf_geometric as tfg import tensorflow as tf import numpy as np from tf_geometric.utils.graph_utils import convert_edge_to_directed # ==================================== Graph Data Structure ==================================== # In tf_geometric, the data of a graph can be represented by either a collections of # tensors (numpy.ndarray or tf.Tensor) or a tfg.Graph object. # A graph usually consists of x(node features), edge_index and edge_weight(optional) # Node Features => (num_nodes, num_features) x = np.random.randn(5, 20).astype(np.float32) # 5 nodes, 20 features # Edge Index => (2, num_edges) # Each column of edge_index (u, v) represents an directed edge from u to v. # Note that it does not cover the edge from v to u. You should provide (v, u) to cover it. # This is not convenient for users. # Thus, we allow users to provide edge_index in undirected form and convert it later. # That is, we can only provide (u, v) and convert it to (u, v) and (v, u) with `convert_edge_to_directed` method. edge_index = np.array([ [0, 0, 1, 3], [1, 2, 2, 1] ]) # Edge Weight => (num_edges) edge_weight = np.array([0.9, 0.8, 0.1, 0.2]).astype(np.float32) # Make the edge_index directed such that we can use it as the input of GCN edge_index, [edge_weight] = convert_edge_to_directed(edge_index, [edge_weight]) # We can convert these numpy array as TensorFlow Tensors and pass them to gnn functions outputs = tfg.nn.gcn( tf.Variable(x), tf.constant(edge_index), tf.constant(edge_weight), tf.Variable(tf.random.truncated_normal([20, 2])) # GCN Weight ) print(outputs) # Usually, we use a graph object to manager these information # edge_weight is optional, we can set it to None if you don't need it graph = tfg.Graph(x=x, edge_index=edge_index, edge_weight=edge_weight) # You can easily convert these numpy arrays as Tensors with the Graph Object API graph.convert_data_to_tensor() # Then, we can use them without too many manual conversion outputs = tfg.nn.gcn( graph.x, graph.edge_index, graph.edge_weight, tf.Variable(tf.random.truncated_normal([20, 2])), # GCN Weight cache=graph.cache # GCN use caches to avoid re-computing of the normed edge information ) print(outputs) # For algorithms that deal with batches of graphs, we can pack a batch of graph into a BatchGraph object # Batch graph wrap a batch of graphs into a single graph, where each nodes has an unique index and a graph index. # The node_graph_index is the index of the corresponding graph for each node in the batch. # The edge_graph_index is the index of the corresponding edge for each node in the batch. batch_graph = tfg.BatchGraph.from_graphs([graph, graph, graph, graph]) # We can reversely split a BatchGraph object into Graphs objects graphs = batch_graph.to_graphs() # Graph Pooling algorithms often rely on such batch data structure # Most of them accept a BatchGraph's data as input and output a feature vector for each graph in the batch outputs = tfg.nn.mean_pool(batch_graph.x, batch_graph.node_graph_index, num_graphs=batch_graph.num_graphs) print(outputs) # We provide some advanced graph pooling operations such as topk_pool node_score = tfg.nn.gcn( batch_graph.x, batch_graph.edge_index, batch_graph.edge_weight, tf.Variable(tf.random.truncated_normal([20, 1])), # GCN Weight cache=graph.cache # GCN use caches to avoid re-computing of the normed edge information ) node_score = tf.reshape(node_score, [-1]) topk_node_index = tfg.nn.topk_pool(batch_graph.node_graph_index, node_score, ratio=0.6) print(topk_node_index) # ==================================== Built-in Datasets ==================================== # all graph data are in numpy format train_data, valid_data, test_data = tfg.datasets.PPIDataset().load_data() # we can convert them into tensorflow format test_data = [graph.convert_data_to_tensor() for graph in test_data] # ==================================== Basic OOP API ==================================== # OOP Style GCN (Graph Convolutional Network) gcn_layer = tfg.layers.GCN(units=20, activation=tf.nn.relu) for graph in test_data: # Cache can speed-up GCN by caching the normed edge information outputs = gcn_layer([graph.x, graph.edge_index, graph.edge_weight], cache=graph.cache) print(outputs) # OOP Style GAT (Multi-head Graph Attention Network) gat_layer = tfg.layers.GAT(units=20, activation=tf.nn.relu, num_heads=4) for graph in test_data: outputs = gat_layer([graph.x, graph.edge_index]) print(outputs) # OOP Style Multi-layer GCN Model class GCNModel(tf.keras.Model): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.gcn0 = tfg.layers.GCN(16, activation=tf.nn.relu) self.gcn1 = tfg.layers.GCN(7) self.dropout = tf.keras.layers.Dropout(0.5) def call(self, inputs, training=None, mask=None, cache=None): x, edge_index, edge_weight = inputs h = self.dropout(x, training=training) h = self.gcn0([h, edge_index, edge_weight], cache=cache) h = self.dropout(h, training=training) h = self.gcn1([h, edge_index, edge_weight], cache=cache) return h gcn_model = GCNModel() for graph in test_data: outputs = gcn_model([graph.x, graph.edge_index, graph.edge_weight], cache=graph.cache) print(outputs) # ==================================== Basic Functional API ==================================== # Functional Style GCN # Functional API is more flexible for advanced algorithms # You can pass both data and parameters to functional APIs gcn_w = tf.Variable(tf.random.truncated_normal([test_data[0].num_features, 20])) for graph in test_data: outputs = tfg.nn.gcn(graph.x, edge_index, edge_weight, gcn_w, activation=tf.nn.relu) print(outputs) # ==================================== Advanced Functional API ==================================== # Most APIs are implemented with Map-Reduce Style # This is a gcn without without weight normalization and transformation # Just pass the mapper/reducer/updater functions to the Functional API for graph in test_data: outputs = tfg.nn.aggregate_neighbors( x=graph.x, edge_index=graph.edge_index, edge_weight=graph.edge_weight, mapper=tfg.nn.identity_mapper, reducer=tfg.nn.sum_reducer, updater=tfg.nn.sum_updater ) print(outputs)

## Cite

If you use tf_geometric in a scientific publication, we would appreciate citations to the following paper:

@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} }

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