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pygmtools provides graph matching solvers in Python API and supports numpy and pytorch backends. pygmtools also provides dataset API for standard graph matching benchmarks.
pygmtools
is published in JMLR! Please cite our paper
if our tools are useful in your research!
pygmtools
(Python Graph Matching Tools) provides graph matching solvers in Python and is easily accessible via:
$ pip install pygmtools
Official documentation: https://pygmtools.readthedocs.io
Source code: https://github.com/Thinklab-SJTU/pygmtools
Graph matching is a fundamental yet challenging problem in pattern recognition, data mining, and others. Graph matching aims to find node-to-node correspondence among multiple graphs, by solving an NP-hard combinatorial optimization problem.
Doing graph matching in Python used to be difficult, and this library wants to make researchers' lives easier.
To highlight, pygmtools
has the following features:
numpy
which is universally accessible, and some state-of-the-art deep
learning architectures with GPU support:
pytorch
, paddle
, jittor
, tensorflow
, mindspore
;You can install the stable release on PyPI:
$ pip install pygmtools
or get the latest version by running:
$ pip install -U https://github.com/Thinklab-SJTU/pygmtools/archive/master.zip # with --user for user install (no root)
Now the pygmtools is available with the numpy
backend.
The following packages are required, and shall be automatically installed by pip
:
Python >= 3.8
requests >= 2.25.1
scipy >= 1.4.1
Pillow >= 7.2.0
numpy >= 1.18.5
easydict >= 1.7
appdirs >= 1.4.4
tqdm >= 4.64.1
networkx >= 2.8.8
aiohttp
async-timeout
This library offers user-friendly API for the following solvers:
This library is designed to support multiple backends with the same set of API. Please follow the official instructions to install your backend.
The following backends are available:
Numpy | PyTorch | Jittor | PaddlePaddle | Tensorflow | MindSpore | |
---|---|---|---|---|---|---|
Linear Solvers | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Classic Solvers | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Multi-Graph Solvers | ✔ | ✔ | ✔ | ✔ | 📆 | 📆 |
Neural Solvers | ✔ | ✔ | ✔ | ✔ | 📆 | 📆 |
Examples Gallery | ✔ | ✔ | ✔ | ✔ | 📆 | 📆 |
✔: Supported; 📆: Planned for future versions (contributions welcomed!).
For more details, please read the documentation.
The library includes several neural network solvers. The pretrained models shall be automatically downloaded upon
needed from Google Drive. If you are experiencing issues accessing Google Drive, please download the pretrained models
manually and put them at ~/.cache/pygmtools
(for Linux).
Available at: [google drive] [baidu drive]
pygmtools
is also featured with a standard data interface of several graph matching benchmarks. Please read
the corresponding documentation for details.
We also maintain a repository containing non-trivial implementation of deep graph matching models, please check out ThinkMatch if you are interested!
If you have any questions, or if you are experiencing any issues, feel free to raise an issue on GitHub.
We also offer the following chat rooms if you are more comfortable with them:
Any contributions/ideas/suggestions from the community is welcomed! Before starting your contribution, please read the Contributing Guide.
pygmtools
is developed and maintained by members from ThinkLab at
Shanghai Jiao Tong University.
pygmtools
is published on Journal of Machine Learning Research (JMLR). If you find our toolkit helpful in your
research, please cite:
Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, and Junchi Yan.
Pygmtools: A Python Graph Matching Toolkit.
Journal of Machine Learning Research, 25(33):1−7, 2024.
In Bibtex format:
@article{wang2024pygm,
author = {Runzhong Wang and Ziao Guo and Wenzheng Pan and Jiale Ma and Yikai Zhang and Nan Yang and Qi Liu and Longxuan Wei and Hanxue Zhang and Chang Liu and Zetian Jiang and Xiaokang Yang and Junchi Yan},
title = {Pygmtools: A Python Graph Matching Toolkit},
journal = {Journal of Machine Learning Research},
year = {2024},
volume = {25},
number = {33},
pages = {1-7},
url = {https://jmlr.org/papers/v25/23-0572.html},
}
[1] Sinkhorn, Richard, and Paul Knopp. "Concerning nonnegative matrices and doubly stochastic matrices." Pacific Journal of Mathematics 21.2 (1967): 343-348.
[2] Munkres, James. "Algorithms for the assignment and transportation problems." Journal of the Society for Industrial and Applied Mathematics 5.1 (1957): 32-38.
[3] Leordeanu, Marius, and Martial Hebert. "A spectral technique for correspondence problems using pairwise constraints." International Conference on Computer Vision (2005).
[4] Cho, Minsu, Jungmin Lee, and Kyoung Mu Lee. "Reweighted random walks for graph matching." European conference on Computer Vision (2010).
[5] Leordeanu, Marius, Martial Hebert, and Rahul Sukthankar. "An integer projected fixed point method for graph matching and map inference." Advances in Neural Information Processing Systems 22 (2009).
[6] Yan, Junchi, et al. "Multi-graph matching via affinity optimization with graduated consistency regularization." IEEE Transactions on Pattern Analysis and Machine Intelligence 38.6 (2015): 1228-1242.
[7] Jiang, Zetian, Tianzhe Wang, and Junchi Yan. "Unifying offline and online multi-graph matching via finding shortest paths on supergraph." IEEE Transactions on Pattern Analysis and Machine Intelligence 43.10 (2020): 3648-3663.
[8] Solé-Ribalta, Albert, and Francesc Serratosa. "Graduated assignment algorithm for multiple graph matching based on a common labeling." International Journal of Pattern Recognition and Artificial Intelligence 27.01 (2013): 1350001.
[9] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Unsupervised Learning of Graph Matching with Mixture of Modes via Discrepancy Minimization." IEEE Transactions on Pattern Analysis and Machine Intelligence 45.8 (2023): 10500-10518.
[10] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Combinatorial learning of robust deep graph matching: an embedding based approach." IEEE Transactions on Pattern Analysis and Machine Intelligence 45.6 (2023): 6984-7000.
[11] Yu, Tianshu, et al. "Learning deep graph matching with channel-independent embedding and hungarian attention." International Conference on Learning Representations. 2019.
[12] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Neural graph matching network: Learning lawler’s quadratic assignment problem with extension to hypergraph and multiple-graph matching." IEEE Transactions on Pattern Analysis and Machine Intelligence 44.9 (2022): 5261-5279.
[13] Wang, Runzhong, Junchi Yan, and Xiaokang Yang. "Combinatorial Learning of Graph Edit Distance via Dynamic Embedding." IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021): 5241-5250.
FAQs
pygmtools provides graph matching solvers in Python API and supports numpy and pytorch backends. pygmtools also provides dataset API for standard graph matching benchmarks.
We found that pygmtools demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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