torch-ppr

This package allows calculating page-rank and personalized page-rank via power iteration with PyTorch, which also supports calculation on GPU (or other accelerators).

💪 Getting Started

As a simple example, consider this simple graph with five nodes.

Its edge list is given as

>>> import torch
>>> edge_index = torch.as_tensor(data=[(0, 1), (1, 2), (1, 3), (2, 4)]).t()

We can use

>>> from torch_ppr import page_rank
>>> page_rank(edge_index=edge_index)
tensor([0.1269, 0.3694, 0.2486, 0.1269, 0.1281])

to calculate the page rank, i.e., a measure of global importance. We notice that the central node receives the largest importance score, while all other nodes have lower importance. Moreover, the two indistinguishable nodes 0 and 3 receive the same page rank.

We can also calculate personalized page rank which measures importance from the perspective of a single node. For instance, for node 2, we have

>>> from torch_ppr import personalized_page_rank
>>> personalized_page_rank(edge_index=edge_index, indices=[2])
tensor([[0.1103, 0.3484, 0.2922, 0.1103, 0.1388]])

Thus, the most important node is the central node 1, nodes 0 and 3 receive the same importance value which is below the value of the direct neighbor 4.

By the virtue of using PyTorch, the code seamlessly works on GPUs, too, and supports auto-grad differentiation. Moreover, the calculation of personalized page rank supports automatic batch size optimization via torch_max_mem.

🚀 Installation

The most recent release can be installed from PyPI with:

$ pip install torch_ppr

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/mberr/torch-ppr.git

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

$ git clone git+https://github.com/mberr/torch-ppr.git
$ cd torch-ppr
$ pip install -e .

$ tox

$ git clone git+https://github.com/mberr/torch-ppr.git
$ cd torch-ppr
$ tox -e docs
$ open docs/build/html/index.html

$ tox -e finish