Skip to main content

An efficient PyTorch implementation of GAT (Graph Attention Transformer) Networks.

Project description

graphattention

PyTorch implementation of Graph Attention Networks.

Project details


Download files

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

Source Distribution

graphattention-0.0.1.tar.gz (4.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

graphattention-0.0.1-py3-none-any.whl (3.1 kB view details)

Uploaded Python 3

File details

Details for the file graphattention-0.0.1.tar.gz.

File metadata

  • Download URL: graphattention-0.0.1.tar.gz
  • Upload date:
  • Size: 4.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for graphattention-0.0.1.tar.gz
Algorithm Hash digest
SHA256 5f14f7e5d60c95b45331522b49b62da8f53b5732cb5a5ba1e32aad70954cac5e
MD5 46a992c68d57b3b9be14af437f90f1ac
BLAKE2b-256 59d1f9f8f943eec81fbeb3b8b39a41223aa063e4425aedc9c0642330e5de94bb

See more details on using hashes here.

File details

Details for the file graphattention-0.0.1-py3-none-any.whl.

File metadata

  • Download URL: graphattention-0.0.1-py3-none-any.whl
  • Upload date:
  • Size: 3.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.6

File hashes

Hashes for graphattention-0.0.1-py3-none-any.whl
Algorithm Hash digest
SHA256 1d160e0c8683a39b9137eea0f15c8f4b08f55b79bf22f1fe4c8d689471dd4ea9
MD5 6a5975485a4ad21dffef9fa7851b8e7d
BLAKE2b-256 f2839371a3271c951f0bebf6c4eadcff7765111b1bf51da292fd8f4e46091538

See more details on using hashes here.

Supported by

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