Adversarial Separation Network for Cross-Network Node Classification

This repository contains the author's implementation in Pytorch for the paper "Adversarial Separation Network for Cross-Network Node Classification".

Environment Requirement

• Python: 3.6

• PyTorch: 1.5.1 (with suitable CUDA and CuDNN version)

• tensorboard: 2.3.0

• Scipy:1.2.1

• Numpy:1.16.2

• sklearn:0.21.1

Datasets:

The data folder includes different domain data.

The preprocessed data can be found in our repository.

• /data/acmv9.mat

• /data/dblpv7.mat

• /data/citationv1.mat

The orginal datasets can be founded from https://www.aminer.cn/citation.

Training:

You can run the command in run.sh to train and evaluate on each task for network graph dataset.

Before that, you need to change the data_root (data root path), learning rate and cuda (gpu options) in the script.

python main.py --data_src 'dblpv7' --data_trg 'acmv9' --lr 3e-2 --cuda '0'

Results

Contact

• zhangxw@smail.nju.edu.cn
• duyuntao@smail.nju.edu.cn

Reference