ContinuousGraphFlow

This is the official implementation for paper:

Continuous Graph Flow

Zhiwei Deng*, Megha Nawhal*, Lili Meng and Greg Mori

Published in ICML 2020 Workshop on Graph Representation Learning and Beyond

Webpage

If you find this code helpful in your research, please cite

@inproceedings{deng2020continuous,
  title={Continuous graph flow},
  author={Deng, Zhiwei and Nawhal, Megha and Meng, Lili and Mori, Greg},
  booktitle={Proceedings of the International Conference on Machine Learning Workshop on Graph Representation Learning and Beyond},
  year={2020}
}

Contents

1. Overview
2. Getting started
3. Sample Usage
4. Results
5. Contact

Overview

We propose a flow-based generative model for graph-structured data, termed Continuous Graph Flow (CGF). CGF is formulated as a system of ordinary differential equations (reversible), uses Continuous Message Passing to transform node states over time (continuous). Highlights for the model: extending flow models to handle variable input dimensions; ability to model reusable dependencies in among dimensions; reversible and memory-efficient.

Getting started

• Clone repository
• Use python 3 and create a virtual environment
• Install dependencies listed in requirements.txt

Sample Usage

The repository contains code for three applications: image puzzle generation, scene layout generation and graph generation. We provide sample commands here. Please change the parameters according to the experiments.

• For image puzzle generation, run the sample command. Change the parameters for exploring the code.

python src/puzzle_graph/train_graphflow.py --data /data --dims 64,64 --strides 1,1,1 --num_blocks 2 
--layer_type concat --rademacher True --graphflow True --ifpuzzle True --num_layers 1 --rtol 1e-5 
--atol 1e-5 --save ./output --batch_size 64 --puzzle_size 2 --imagesize 64 --num_epochs 1000000 
--lr 0.00001 --patch_size 16 --graph_multiscale True --multiscale_method conv --conv True

• For graph generation, run the following command.

python src/graph_generation/train_graphflow.py  --graph_type community_small --dims 32 --num_blocks 2 
--result_dir ./output --save ./output --lr 1e-5 --use_logit True

Results

Graph generation:

Scene layouts generation:

Contact

For further questions, please contact the authors Zhiwei Deng or Megha Nawhal.