Permutation Invariant Graph Generation via Score-Based Generative Modeling
This repo contains the official implementation for the paper
Permutation Invariant Graph Generation via Score-Based Generative Modeling (AISTATS 2020),
Authors: Chenhao Niu, Yang Song, Jiaming Song, Shengjia Zhao, Aditya Grover, Stefano Ermon
We propose a permutation invariant approach to modeling graphs, using the framework of score-based generative modeling. In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph (a.k.a, the score function). This permutation equivariant model of gradients implicitly defines a permutation invariant distribution for graphs. We can train this graph neural network with score matching and sample from it with annealed Langevin dynamics.
Dependencies
First, install PyTorch following the steps on its official website. The code has been tested over PyTorch 1.3.1 and 1.8.1.
Then run the following command to install the other dependencies.
pip install -r requirements.txt
To compile the ORCA program (see http://www.biolab.si/supp/orca/orca.html) for the evaluation step, run
cd evaluation/orca && g++ -O2 -std=c++11 -o orca orca.cpp
Running Experiments
Preparing Datasets
To generate the datasets, run
mkdir data
python gen_data.py # to generate the community-small dataset
python process_dataset.py # to generate the ego-small dataset
Configuring
The configurations are in the config/ directory, written in the YAML format. Refer to the comments in the given files for details.
The output files under the directory <exp_dir>/<exp_name> (set in the YAML configuration file) are
.
├── config.yaml # a copy of the configuration
├── fig # reconstruction of the perturbed graphs
│ └── xxx.pdf
├── info.log # logs (if running train.py)
├── models
│ └── xxx.pth # the saved PyTorch checkpoint
└── sample
├── fig
│ └── xxx.pdf # images of the generated graphs
├── info.log # logs (if running sampling.py)
└── sample_data
└── xxx.pkl # saved python list object of the generated graphs (networkx.Graph)
Training
train.py is the main executable file to run the whole pipeline (training, sampling, evaluation). Run python train.py -h to show its usage:
usage: train.py [-h] -c CONFIG_FILE [-l LOG_LEVEL] [-m COMMENT]
Running Experiments
optional arguments:
-h, --help show this help message and exit
-c CONFIG_FILE, --config_file CONFIG_FILE
Path of config file
-l LOG_LEVEL, --log_level LOG_LEVEL
Logging Level, one of: DEBUG, INFO, WARNING, ERROR, CRITICAL
-m COMMENT, --comment COMMENT
A single line comment for the experiment
Examples:
python train.py -c config/train_ego_small.yaml # to run on Ego-small dataset
python train.py -c config/train_com_small.yaml # to run on Community-small dataset
Sampling
sample.py is for evaluating a saved model. The usage is the same as train.py. To set the location of the saved model, change model_save_dir in the YAML file, e.g. model_save_dir: 'exp/ego_small/edp-gnn_ego_small_xxx/models'.
Examples:
python sample.py -c config/sample_ego_small.yaml # to run on Ego-small dataset
python sample.py -c config/sample_com_small.yaml # to run on Community-small dataset
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