GraphGT: Machine Learning Datasets for Graph Generation and Transformation

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Deep Learning GraphGT
Overview

GraphGT: Machine Learning Datasets for Graph Generation and Transformation

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Dataset Website | Paper

Installation

Using pip

To install the core environment dependencies of GraphGT, use pip:

pip install GraphGT

Note: GraphGT is in the beta release. Please update your local copy regularly by

pip install GraphGT --upgrade

DataLoader

import graphgt 
dataloader = graphgt.DataLoader(name=KEY, save_path='./', format='numpy')

KEY: 'qm9', 'zinc', 'moses', 'chembl', 'profold', 'kinetics', 'ntu', 'collab', 'n_body_charged', 'n_body_spring', 'random_geometry', 'waxman', 'traffic_bay', 'traffic_la', 'scale_free_{10|20|50|100}', 'ER_{20|40|60}', 'IoT_{20|40|60}', 'authen'.

Cite Us

If you use our dataset in your work, please cite us:

@article{graphgt,
  title={GraphGT: Machine Learning Datasets for Graph Generation and Transformation},
  author={Du, Yuanqi and Wang, Shiyu and Guo, Xiaojie and Cao, Hengning and Jiang, Junji and Hu, Shujie and Varala, Aishwarya and Angirekula, Abhinav and Zhao, Liang},
  year={2021}
}

Team

Yuanqi Du (Leader), Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao (Advisor)

Contact

Send us an email or open an issue.

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Comments
  • Questions about using graphGT in the model

    Questions about using graphGT in the model

    Hello, thank you very much for your work. I just started to learn graph generation technology, and I noticed that there are graphvae models in the code, but I looked carefully and didn't seem to have any connection with GraphGT. I've downloaded the n_body_charged dataset. But I found that the code also needs a file like train.dat, which seems to require further data processing on the dataset. Could you give me some guidance on how to further apply GraphGT in graphvae？

    opened by RENEK-bool 2
  • Use API for Own Dataset

    Use API for Own Dataset

    Hi @yuanqidu!

    Love your work! The area of graph generation is of special interest to me as well. Specially, fixed-sized generation for skeleton datasets. I have a quick question. I have my own skeleton graph dataset, that I would like to use for the graph generation task, do you have a quick summation of how I would go about doing that?

    Thank you in advance! I'm sure you're plenty busy!

    Jess

    opened by jessxphil 1
  • brain network usage problem

    brain network usage problem

    Hello, thank you very much for your work. I have downloaded the brain networks dataset, but I found some problems: 1. FC in the dataset_ concatenated_ adj、SC_ concatenated_ The data in adj is all 0, which seems unreasonable. 2、FC_ concatenated_ edge_ feat、SC_ concatenated_ edge_ What the fourth dimension of feat stands for is missing. 3. There is no explanation for selecting 68 ROIs. These problems have brought me some difficulties in using the data set. Can you help me? Thank you.

    opened by ws0905 4
  • Reproducibility of benchmark results for generation and transformation tasks?

    Reproducibility of benchmark results for generation and transformation tasks?

    Hello!

    Thank you for the tutorial notebooks showing how to download and instantiate the various datasets.

    I was wondering whether you had any code or documentation as to how to reproduce the benchmark results reported in Table 2 and especially Table 3 in the full paper? I'm interested in using some of the graph transformation data and want to have a reference point for the baseline results you reported.

    It seems like the modeling and evaluation code for the two transformation benchmark/baselines is located at IN/model.py and NEC-DGT/main.py, but is not necessarily "plug and play" or configured for automated setup. For example, IN/model.py#L148 seems to require that the MolOpt data is downloaded and named appropriately before the Interaction Network benchmarking code will run.

    1. Is the MolOpt data in the GraphGT release? If so what is the name (in {'qm9', 'zinc', 'moses', 'chembl', 'profold' ... }) and
    2. What are the naming and pathing conventions for the transformation datasets to get the IN/model.py and the NEC-DGT/main.py codes both running?

    Overall I just want to confirm that this is where the work representing how to try and reproduce your benchmark results is located, and any extra guidance you can provide on how to set it up. If it is mostly just for historical parsing, and not really workable at this point (I see some 2018 dates on certain files), that would be good to know too.

    Thanks!

    opened by jwkirchenbauer 3
Owner
y6q9
Visiting Student at AMLAB with Prof. Max Welling, working on Deep Graph Learning, Deep Generative Models, AI for Science
y6q9
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