Citation
========

If you find JraphX useful for your research, please consider citing it along with the foundational libraries it builds upon.

JraphX
------

.. code-block:: bibtex

   @software{jraphx2025github,
     author = {David Braun},
     title = {{JraphX}: A Graph Neural Network library for {JAX}/{Flax NNX}},
     url = {https://github.com/DBraun/jraphx},
     year = {2025},
   }

PyTorch Geometric
-----------------

JraphX incorporates substantial portions of code and documentation from `PyTorch Geometric <https://github.com/pyg-team/pytorch_geometric/>`_:

.. code-block:: bibtex

    @inproceedings{Fey/Lenssen/2019,
      title={Fast Graph Representation Learning with {PyTorch Geometric}},
      author={Fey, Matthias and Lenssen, Jan E.},
      booktitle={ICLR Workshop on Representation Learning on Graphs and Manifolds},
      year={2019},
    }

    @inproceedings{Fey/etal/2025,
      title={{PyG} 2.0: Scalable Learning on Real World Graphs},
      author={Fey, Matthias and Sunil, Jinu and Nitta, Akihiro and Puri, Rishi and Shah, Manan, and Stojanovi{\v{c}, Bla{\v{z} and Bendias, Ramona and Alexandria, Barghi and Kocijan, Vid and Zhang, Zecheng and He, Xinwei and Lenssen, Jan E. and Leskovec, Jure},
      booktitle={Temporal Graph Learning Workshop @ KDD},
      year={2025},
    }

DeepMind Jraph
--------------

JraphX serves as an unofficial successor to DeepMind's `jraph <https://github.com/google-deepmind/jraph>`_ library:

.. code-block:: bibtex

    @software{jraph2020github,
      author = {Jonathan Godwin* and Thomas Keck* and Peter Battaglia and Victor Bapst and Thomas Kipf and Yujia Li and Kimberly Stachenfeld and Petar Veli\v{c}kovi\'{c} and Alvaro Sanchez-Gonzalez},
      title = {{J}raph: {A} library for graph neural networks in jax.},
      url = {http://github.com/deepmind/jraph},
      version = {0.0.1.dev},
      year = {2020},
    }