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Documentation | Case Study | Data Set Descriptions | Installation | Data Structures | External Resources | Paper


PyTorch Geometric Signed Directed is a signed and directed extension library for PyTorch Geometric. It follows the package structure in PyTorch Geometric Temporal.

<p align="justify">The library consists of various signed and directed geometric deep learning, embedding, and clustering methods from a variety of published research papers and selected preprints.

We also provide detailed examples in the examples folder.


Citing

If you find PyTorch Geometric Signed Directed useful in your research, please consider adding the following citation:

@inproceedings{he2024pytorch,
  title={Pytorch Geometric Signed Directed: A software package on graph neural networks for signed and directed graphs},
  author={He, Yixuan and Zhang, Xitong and Huang, Junjie and Rozemberczki, Benedek and Cucuringu, Mihai and Reinert, Gesine},
  booktitle={Learning on Graphs Conference},
  pages={12--1},
  year={2024},
  organization={PMLR}
}

Methods Included

In detail, the following signed or directed graph neural networks, as well as related methods designed for signed or directed netwroks, were implemented.

Directed Unsigned Network Models and Layers

<details> <summary><b>Expand to see all methods implemented for directed networks...</b></summary> </details>

Signed (Directed) Network Models and Layers

<details> <summary><b>Expand to see all methods implemented for signed networks...</b></summary> </details>

Network Generation Methods

Data Loaders and Classes

<details> <summary><b>Expand to see all data loaders and related methods...</b></summary> </details>

Task-Specific Objectives and Evaluation Methods

<details> <summary><b>Expand to see all task-specific objectives and evaluation methods...</b></summary> </details>

Utilities and Preprocessing Methods

<details> <summary><b>Expand to see all utilities and preprocessing methods...</b></summary> </details>

Head over to our documentation to find out more! If you notice anything unexpected, please open an issue. If you are missing a specific method, feel free to open a feature request.


Installation

Binaries are provided for Python version >= 3.7 and NetworkX version < 2.7.

After installing PyTorch and PyG, simply run

pip install torch-geometric-signed-directed

Running tests

$ python setup.py test

License