Skip to main content

Edge representation learning library

Project description

PyTorch-Geometric Edge

Unit tests Lint

PyTorch-Geometric Edge (PyGE) is a library that implements models for learning vector representations of graph edges. It is build upon the popular PyG library and allows to mix layers and models from both libraries in the same code. Note that PyGE is still under development and model APIs may change in future revisions.

Installation

Currently, we test our code using Python 3.8, but newer versions should work fine as well. We plan to extend our test suite in upcoming releases. The same applies for the PyTorch version – we currently run our tests using PyTorch 1.10.0.

For a detailed list of required packages, please take a look at the setup.py file, where library versions are constrained.

You can install PyGE using pip:

$ pip install torch-geometric-edge

Available models

Currently, we implement the following edge-centric models:

  • non-trainable node pair operators: Average, Hadamard, L1, L2
  • node2edge and edge2node layers
  • Line2vec
  • AttrE2vec
  • PairE
  • Dual Hypergraph Transformation

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

torch_geometric_edge-0.0.1-py3-none-any.whl (24.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page