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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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Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling

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