Attention Walk

A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018).

Abstract

This repository provides an implementation of Attention Walk as described in the paper:

Watch Your Step: Learning Node Embeddings via Graph Attention. Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi. NIPS, 2018. [Paper]

The original Tensorflow implementation is available [here].

Requirements

The codebase is implemented in Python 3.5.2. package versions used for development are just below.

networkx          2.4
tqdm              4.28.1
numpy             1.15.4
pandas            0.23.4
texttable         1.5.0
scipy             1.1.0
argparse          1.1.0
torch             1.1.0
torchvision       0.3.0

Datasets

Learning of the embedding is handled by the src/main.py script which provides the following command line arguments.

Input and output options

  --edge-path         STR   Input graph path.     Default is `input/chameleon_edges.csv`.
  --embedding-path    STR   Embedding path.       Default is `output/chameleon_AW_embedding.csv`.
  --attention-path    STR   Attention path.       Default is `output/chameleon_AW_attention.csv`.

Model options

  --dimensions           INT       Number of embeding dimensions.        Default is 128.
  --epochs               INT       Number of training epochs.            Default is 200.
  --window-size          INT       Skip-gram window size.                Default is 5.
  --learning-rate        FLOAT     Learning rate value.                  Default is 0.01.
  --beta                 FLOAT     Attention regularization parameter.   Default is 0.5.
  --gamma                FLOAT     Embedding regularization parameter.   Default is 0.5.
  --num-of-walks         INT       Number of walks per source node.      Default is 80.

Examples

Creating an Attention Walk embedding of the default dataset with 256 dimensions.

python src/main.py --dimensions 256

Creating an Attention Walk embedding of the default dataset with a higher window size.

python src/main.py --window-size 20

Creating an embedding of another dataset the Twitch Brasilians. Saving the outputs under custom file names.

python src/main.py --edge-path input/ptbr_edges.csv --embedding-path output/ptbr_AW_embedding.csv --attention-path output/ptbr_AW_attention.csv

License

• GNU License