MNE(Multiplex Network Embedding)

This is the source code for IJCAI 2018 paper "Scalable Multiplex Network Embedding".

The readers are welcome to star/fork this repository and use it to train your own model, reproduce our experiment, and follow our future work. Please kindly cite our paper:

@inproceedings{zhang2018MNE,
  author    = {Hongming Zhang and
               Liwei Qiu and
               Lingling Yi and
               Yangqiu Song},
  title     = {Scalable Multiplex Network Embedding},
  booktitle = {Proceedings of the Twenty-Seventh International Joint Conference on
               Artificial Intelligence, {IJCAI} 2018, July 13-19, 2018, Stockholm,
               Sweden.},
  pages     = {3082--3088},
  year      = {2018},
  url       = {https://doi.org/10.24963/ijcai.2018/428},
  doi       = {10.24963/ijcai.2018/428},
  timestamp = {Sat, 28 Jul 2018 14:39:21 +0200}
}

Note that due to the size limitation of the repository, we only provide few small datasets to help you understand our code and reproduce our experiment. You are welcome to download those largest datasets by yourself or use your own dataset.

Requirement

Python 3
networkx >= 1.11
sklearn >= 0.18.1
gensim >= 3.4

Dataset

Here we provide Vickers dataset as an example, you can download all the other datasets from Twitter Higgs，Multiplex (old), or Multiplex (new). You can also use your own multiplex network dateset, as long as it fits the following template.

edge_type head tail weight
    r1     n1   n2    1
    r2     n2   n3    1
    .
    .
    .

Model

Before training, you should first create a folder 'Model' with:

mkdir Model

The embedding model will be automatically saved into that folder.

Train

To train the embedding model, simply run:

python3 train_model.py data/Vickers-Chan-7thGraders_multiplex.edges

You can replace the name of provided dataset with your own dataset.

Demo

To repeat the experiment in the paper, simply run:

python3 main.py data/Vickers-Chan-7thGraders_multiplex.edges

Acknowledgment

We built the training framework based on the original Gensim Word2Vec. We used the code from LINE, Node2Vec, and algorithm from PMNE to complete our experiment.

Others

If you have some questions about the code, you are welcome to open an issue or send me an email, I will respond to that as soon as possible.