DeepGD: A Deep Learning Framework for Graph Drawing Using GNN

This repo contains a simple demonstration for the IEEE CG&A21 paper entitled "DeepGD: A Deep Learning Framework for Graph Drawing Using GNN". It includes:

• a dataloader for Rome Graphs dataset,
• a basic implementation of DeepGD model,
• and a demo notebook that shows how to train DeepGD models from scratch with minimal amount of codes.

Environment

This code has been tested on python3.10 + cuda11.8 + pytorch2.0 + pyg2.3. Anaconda is suggested for managing dependencies, as installing pyg with pip can be tricky.

Configuration

The default hyper-parameters of the model have been configured to reproduce the best performance reported in the DeepGD paper.

However, the layout initializer for the dataset is by default nx.drawing.random_layout, which is not PivotMDS that yields the best results shown in the paper (random initialization already produces good enough results though). Feel free to modify this behavior by passing a different initializer with dataset = RomeDataset(layout_initializer=something_else). A custom initializer can be any function that follows the behavior of nx.drawing.random_layout.

Training

With Nvidia V100, each training epoch takes 30s on average. It takes around 600 epochs to completly converge.

Evaluation

For evaluation on custom data, the easiest way is to subclass RomeDataset and override raw_file_names and process_raw methods.

Caveat: Even though the behavior of process do not need to be overriden, it is required to have a dummy def process(self): super().process() defined in the subclasses to make it work properly. For details, please refer to pyg.data.InMemoryDataset documentation.

This repo includes a model checkpoint model_stress_only.pt, which reproduces the result for Stress Minimization-only objective function reported in the paper.

For easy benckmarking with DeepGD and a comprehensive set of other baseline methods, please check GraphDrawingBenchmark. This benchmark repo automatically evaluates all common graph drawing metrics on the same train/test split for Rome dataset as used in the DeepGD paper.

🔗 Subsequent Work - SmartGD

Beyond the differentiable metrics optimization explored in this work, we further extend the idea to non-differentiable numeric metrics. We propose a more generalized framework called SmartGD for optimizing arbitrary numeric aesthetic metrics. Please check this repository for more details.

Paper: https://ieeexplore.ieee.org/document/10224347

Citation

If you used our code or find our work useful in your research, please consider citing:

@article{deepgd,
author={Wang, Xiaoqi and Yen, Kevin and Hu, Yifan and Shen, Han-Wei},
journal={IEEE Computer Graphics and Applications},
title={DeepGD: A Deep Learning Framework for Graph Drawing Using GNN},
year={2021},
volume={41},
number={5},
pages={32-44},
doi={10.1109/MCG.2021.3093908}
}