This project contains code related to the paper "Generalized Neural Graph Embedding with Matrix Factorization". G2-EMF is a graph/network embedding method that can incorporate the structure information, content information and label information simultaneously in an unsupervised manner, i.e., without leveraging downstream classifiers. G2-EMF outperforms unsupervised baselines from 10.1% to 34.4% on the node classification task. Details can be accessed here.

• Matlab (>R2014a)
• gcc (>4.4.5)
• networkx

Run the main script in matlab as:

run_emf

We test our model in four datasets in the paper, here in ./data/ folder we provide files of cora dataset. We use dataset splits provided by Planetoid, where data files are formatted as ind.<dataset>.<suffix>, as well as several files processed from the origin files:

• {dataset}.embeddings.walks.0: random walk sequences obtained by directly running DeepWalk
• {dataset}_features.mat: features saved .mat file
• {dataset}_train_data.npy: training nodes and corresponding labels in .npy file

You can specify a dataset by editing run_emf.m, where details about other hyper-parameters can also be found.

We save and test checkpoints at every verbose, and you can change its value to fit your storage.

The final output as well as checkpoints are .mat files which contain matrix W and matrix S described in our paper. Matrix W is the embedding matrix of the input graph with size (num_nodes * emb_dim), and you can refer to test.py to evalute its performance.

The original version of this code base was forked from emf, and we also referred to GCN while preprocessing datasets. Many thanks to the authors for making their code available.

Please cite our paper if you find G2-EMF useful in your research:

@article{guo2017generalized,
  title={Generalized Neural Graph Embedding with Matrix Factorization},
  author={Guo, Junliang and Xu, Linli and Huang, Xunpeng and Chen, Enhong},
  journal={arXiv preprint arXiv:1711.04094},
  year={2017}
}