Large Scale Information Network Embedding (LINE) - PyTorch Implementation

For python 3 and above. This is a toy implementation and should be treated as so.

Description

The LINE algorithm was proposed in 2015 by Jian Tang.

This is a PyTorch implementation, which can be trained on a GPU - following your hardware. At the cost of speed, it also is trainable on CPU.

Usage

It is recommended to run the model within a virtualenv.

Beforehand, install the required dependencies:

$ (env) pip install -r requirements.txt

Run:

python ./train.py -g ./data/erdosrenyi.edgelist -save ./model.pt -lossdata ./loss.pkl -epochs 10

Available hyperparameters are:

• --order: Order 1 or 2 for the LINE algorithm.
• --negativepower: Power used for raising the nodes out-degree distribution.
• --negsamplesize: number of negative examples used. Defaults to 5.
• --batchsize: batchsize during training.
• --epochs: Number of epochs for training.
• --learning_rate: Learning rate aggressiveness.