This package is used for extracting Graph Attention Embeddings and provides a framework for a Tensorflow Graph Attention Layer which can be used for knowledge graph /node base semantic tasks. It determines the pair wise embedding matrix for a higher order node representation and concatenates them with an attention weight. It then passes it through a leakyrelu activation for importance sampling and damps out negative effect of a node.It then applies a softmax layer for normalization of the attention results and determines the final output scores.The GraphAttentionBase.py script implements a Tensorflow/Keras Layer for the GAT which can be used and the GraphMultiheadAttention.py is used to extract GAT embeddings.
1 maintainer
Metadata
1 maintainer
Release files for GraphAttentionNetworks 0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| GraphAttentionNetworks-0.1.tar.gz | 4.2 kB | Details |
Release files / GraphAttentionNetworks-0.1.tar.gz
| Download URL | GraphAttentionNetworks-0.1.tar.gz |
|---|---|
| Size | 4.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
fc349b7e036c984523a3dc77c4222c0287825e9aadf5b8dce5279cfac336dc7b
|
|
BLAKE2b-256 checksum How to use checksums |
eceb1fa7998ac6d0cc21c555771a9acf1ea2f1db06fcb6b351d8b9be2585f404
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.5.0.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.62.2 CPython/3.8.3
|
