CENALP

This repository contains the code of paper:

CENALP: Du, Xingbo & Yan, Junchi & Zha, Hongyuan. (2019). Joint Link Prediction and Network Alignment via Cross-graph Embedding. 2251-2257. 10.24963/ijcai.2019/312.

Before executing CENALP, you should install the following packages:
pip install sklearn
pip install networkx
pip install gensim
pip install tqdm
The detailed version are python==3.7.2 and networkx==2.4, sklearn==0.22.1, gensim==3.4.0, tqdm==4.31.1, but they are not mandatory unless the code doesn't work.

Basic usage

Data

We provide a toy dataset, which is named 'bigtoy'. If you want to evaluate other datasets, please ensure that ground truth alignments and the edges for two networks are necessary. In addition, you can find the datasets used in the paper in http://thinklab.sjtu.edu.cn/paper/IJCAI19_network_dataset.zip.

Example

In order to run CENALP, you can execute demo.py directly or execute the following command in ./src/:
python demo.py
To modify some of the parameters, you can run the code like this:
python demo.py --filename bigtoy --align_train_prop 0.0 --q 0.5
You can check out the other options:
python demo.py --help

Evaluate

We use precision and recall to evaluate both link prediction and network alignment in this repository.

Reference

If you are interested in our researches, please cite our papers:
[1] Du, Xingbo & Yan, Junchi & Zha, Hongyuan. (2019). Joint Link Prediction and Network Alignment via Cross-graph Embedding. 2251-2257. 10.24963/ijcai.2019/312.
[2] Du, Xingbo & Yan, Junchi & Zhang, Rui & Zha, Hongyuan. (2020). Cross-network Skip-gram Embedding for Joint Network Alignment and Link Prediction. IEEE Transactions on Knowledge and Data Engineering. PP. 1-1. 10.1109/TKDE.2020.2997861.