tf-geometric

tf_geometric

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 <https://github.com/CrawlScript/tf_geometric>_
• Documentation: https://tf-geometric.readthedocs.io <https://tf-geometric.readthedocs.io>_ (\ 中文版 <https://tf-geometric.readthedocs.io/en/latest/index_cn.html>_\ )
• Paper: Efficient Graph Deep Learning in TensorFlow with tf_geometric <https://arxiv.org/abs/2101.11552>_

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:

.. code-block:: python

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 = graph.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:

.. code-block:: html

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) <demo/demo_gcn.py>_
• Multi-head Graph Attention Network (GAT) <demo/demo_gat.py>_
• Approximate Personalized Propagation of Neural Predictions (APPNP) <demo/demo_appnp.py>_
• Inductive Representation Learning on Large Graphs (GraphSAGE) <demo/demo_graph_sage_func.py>_
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (ChebyNet) <demo/demo_chebynet.py>_
• Simple Graph Convolution (SGC) <demo/demo_sgc.py>_
• Topology Adaptive Graph Convolutional Network (TAGCN) <demo/demo_tagcn.py>_
• Deep Graph Infomax (DGI) <demo/demo_dgi.py>_
• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification (DropEdge) <demo/demo_drop_edge_gcn.py>_
• Graph Convolutional Networks for Text Classification (TextGCN) <https://github.com/CrawlScript/TensorFlow-TextGCN>_
• Simple Spectral Graph Convolution (SSGC/S^2GC) <demo/demo_ssgc.py>_

Graph Classification ^^^^^^^^^^^^^^^^^^^^

• MeanPooling <demo/demo_mean_pool.py>_
• Graph Isomorphism Network (GIN) <demo/demo_gin.py>_
• Self-Attention Graph Pooling (SAGPooling) <demo/demo_sag_pool_h.py>_
• Hierarchical Graph Representation Learning with Differentiable Pooling (DiffPool) <demo/demo_diff_pool.py>_
• Order Matters: Sequence to Sequence for Sets (Set2Set) <demo/demo_set2set.py>_
• ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (ASAP) <demo/demo_asap.py>_
• An End-to-End Deep Learning Architecture for Graph Classification (SortPool) <demo/demo_sort_pool.py>_
• Spectral Clustering with Graph Neural Networks for Graph Pooling (MinCutPool) <demo/demo_min_cut_pool.py>_

Link Prediction ^^^^^^^^^^^^^^^

• Graph Auto-Encoder (GAE) <demo/demo_gae.py>_

Save and Load Models ^^^^^^^^^^^^^^^^^^^^

• Save and Load Models <demo/demo_save_and_load_model.py>_
• Save and Load Models with tf.train.Checkpoint <demo/demo_checkpoint.py>_

Distributed Training ^^^^^^^^^^^^^^^^^^^^

• Distributed GCN for Node Classification <demo/demo_distributed_gcn.py>_
• Distributed MeanPooling for Graph Classification <demo/demo_distributed_mean_pool.py>_

Sparse ^^^^^^

• Sparse Node Features <demo/demo_sparse_node_features.py>_

Installation

Requirements:

• Operation System: Windows / Linux / Mac OS

• Python: version >= 3.7

• Python Packages:

  • tensorflow/tensorflow-gpu: >= 1.15.0 or >= 2.7.0
  • tf_sparse
  • numpy >= 1.17.4
  • networkx >= 2.1
  • scipy >= 1.1.0

Use one of the following commands below:

.. code-block:: bash

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.

.. code-block:: python

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

==================================== 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)

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

Using 'to_directed' to obtain a graph with directed edges such that we can use it as the input of GCN

graph = tfg.Graph(x=x, edge_index=edge_index, edge_weight=edge_weight).to_directed()

Define a Graph Convolutional Layer (GCN)

gcn_layer = tfg.layers.GCN(4, activation=tf.nn.relu)

Perform GCN on the graph

h = gcn_layer([graph.x, graph.edge_index, graph.edge_weight]) print("Node Representations (GCN on a Graph): \n", h)

for _ in range(10): # Using Graph.cache can avoid recomputation of GCN's normalized adjacency matrix, # which can dramatically improve the efficiency of GCN. h = gcn_layer([graph.x, graph.edge_index, graph.edge_weight], cache=graph.cache)

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, graph])

We can reversely split a BatchGraph object into Graphs objects

graphs = batch_graph.to_graphs()

Define a Graph Convolutional Layer (GCN)

batch_gcn_layer = tfg.layers.GCN(4, activation=tf.nn.relu)

Perform GCN on the BatchGraph

batch_h = gcn_layer([batch_graph.x, batch_graph.edge_index, batch_graph.edge_weight]) print("Node Representations (GCN on a BatchGraph): \n", batch_h)

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

graph_h = tfg.nn.mean_pool(batch_h, batch_graph.node_graph_index, num_graphs=batch_graph.num_graphs) print("Graph Representations (Mean Pooling on a BatchGraph): \n", batch_h)

Define a Graph Convolutional Layer (GCN) for scoring each node

gcn_score_layer = tfg.layers.GCN(1)

We provide some advanced graph pooling operations such as topk_pool

node_score = gcn_score_layer([batch_graph.x, batch_graph.edge_index, batch_graph.edge_weight]) node_score = tf.reshape(node_score, [-1]) print("Score of Each Node: \n", node_score) topk_node_index = tfg.nn.topk_pool(batch_graph.node_graph_index, node_score, ratio=0.6) print("Top-k Node Index (Top-k Pooling): \n", topk_node_index)

==================================== Built-in Datasets ====================================

all graph data are in numpy format

Cora Dataset

graph, (train_index, valid_index, test_index) = tfg.datasets.CoraDataset().load_data()

PPI Dataset

train_data, valid_data, test_data = tfg.datasets.PPIDataset().load_data()

TU Datasets

TU Datasets: https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets

graph_dicts = tfg.datasets.TUDataset("NCI1").load_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, graph.adj(), 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:

.. code-block:: html

@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} }

Related Projects

• MIG-GT: "Modality-Independent Graph Neural Networks with Global Transformers for Multimodal Recommendation" (AAAI 2025). URL: https://github.com/CrawlScript/MIG-GT <https://github.com/CrawlScript/MIG-GT>_.
• RpHGNN: “Efficient Heterogeneous Graph Learning via Random Projection” (TKDE 2024). URL: https://github.com/CrawlScript/RpHGNN <https://github.com/CrawlScript/RpHGNN>_.
• MGDCF: "MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering" (TKDE 2024). URL: https://github.com/CrawlScript/Torch-MGDCF <https://github.com/CrawlScript/Torch-MGDCF>_.
• tf_sparse: We develop TensorFlow Sparse (tf_sparse) <https://github.com/CrawlScript/tf_sparse>_ to implement efficient and elegant sparse TensorFlow operations for tf_geometric. URL: https://github.com/CrawlScript/tf_sparse <https://github.com/CrawlScript/tf_sparse>_.
• GRecX: GRecX <https://github.com/maenzhier/GRecX>_ is an efficient and unified benchmark for GNN-based recommendation. URL: https://github.com/maenzhier/GRecX <https://github.com/maenzhier/GRecX>_.