This repository provides a Tensorflow implementation of the FastGAE framework, introduced in the article FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding. This framework aims to speed up Graph Autoencoders (AE) and Graph Variational Autoencoders (VAE), and at scaling these models to large graphs with millions of nodes and edges.
This work has been accepted for publication in Elsevier's Neural Networks journal.
We provide the FastGAE and Variational FastGAE models from our paper, together with standard Graph Autoencoders and standard Variational Graph Autoencoders from the original article of Kipf and Welling (2016).
We evaluate our framework on the link prediction and node clustering tasks introduced in the paper. We provide the Cora, Citeseer, Pubmed and Google datasets in the data folder. The three additional graphs used in the paper (SBM, Patent and Youtube) will later be provided via external links due to size constraints. We refer to section 4 of the paper for more information about datasets.
Our code builds upon Thomas Kipf's original implementation of graph AE and VAE, and upon previous research works from our team.
python setup.py installRequirements: tensorflow (1.X), networkx, numpy, scikit-learn, scipy
cd fastgae
python train.py --model=gcn_ae --dataset=pubmed --task=link_prediction --fastgae=True --measure=degree --alpha=1.0 --nb_node_samples=1000
python train.py --model=gcn_ae --dataset=pubmed --task=link_predictionThe above commands will train a graph AE on the Pubmed dataset, with (line 2) and without (line 3) the FastGAE framework, decoding random subgraphs of 1000 nodes, drawn via degree-based sampling with alpha=1.0. Models are evaluated on the Link Prediction task, with all parameters set to default values.
As detailed in Table 2 of the paper, line 2 returns competitive average performances w.r.t. line 3 i.e. the standard graph AE, while being significantly faster. We recommend GPU usage for faster learning. Use the --nb_run option to average results over multiple runs.
| Parameter | Type | Description | Default Value |
|---|---|---|---|
model |
string | Name of the model, among: - gcn_ae: Graph AE from Kipf and Welling (2016), with 2-layer GCN encoder and inner product decoder- gcn_vae: Graph VAE from Kipf and Welling (2016), with Gaussian distributions, 2-layer GCN encoders for mu and sigma, and inner product decoder |
gcn_ae |
dataset |
string | Name of the dataset, among: - cora: scientific publications citation network - citeseer: scientific publications citation network - pubmed: scientific publications citation network - google: google.com web graph You can specify any additional graph dataset, in edgelist format, by editing input_data.py |
cora |
task |
string | Name of the Machine Learning evaluation task, among: - link_prediction: Link Prediction - node_clustering: Node Clustering See section 4 of paper for details about tasks |
link_prediction |
fastgae |
boolean | Whether to use the FastGAE framework | False |
nb_node_samples |
int | For FastGAE: number of nodes to sample at each iteration, i.e. sampled subgraph size | 1000 |
measure |
string | For FastGAE: node importance measure used in sampling, among degree, core and uniform |
degree |
alpha |
float | For FastGAE: alpha hyperparameter for core/degree-based sampling | 2.0 |
replace |
boolean | For FastGAE: whether to sample nodes with (True) or without (False) replacement | False |
dropout |
float | Dropout rate | 0. |
iterations |
int | Number of training iterations | 200 |
features |
boolean | Whether to include node features in encoder | False |
learning_rate |
float | Initial learning rate (with Adam optimizer) | 0.01 |
hidden |
int | Number of units in GCN encoder hidden layer | 32 |
dimension |
int | Dimension of encoder output, i.e. embedding dimension | 16 |
nb_run |
integer | Number of model runs + tests | 1 |
prop_val |
float | Proportion of edges in validation set (for Link Prediction) | 5. |
prop_test |
float | Proportion of edges in test set (for Link Prediction) | 10. |
validation |
boolean | Whether to report validation results at each epoch (implemented for Link Prediction task) | False |
verbose |
boolean | Whether to print full comments details | True |
Please cite our paper if you use this code in your own work:
@article{salha2021fastgae,
title={FastGAE: Scalable graph autoencoders with stochastic subgraph decoding},
author={Salha, Guillaume and Hennequin, Romain and Remy, Jean-Baptiste and Moussallam, Manuel and Vazirgiannis, Michalis},
journal={Neural Networks},
volume={142},
pages={1--19},
year={2021},
publisher={Elsevier}
}
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