Analyzing Heterogeneous Network with Missing Attributes by Unsupervised Contrastive Learning

This repository contains the code of the paper:

Analyzing Heterogeneous Network with Missing Attributes by Unsupervised Contrastive Learning

which has been accepted by TNNLS.

Dependencies

• Python3.7
• NumPy
• SciPy
• scikit-learn
• NetworkX
• PyTorch

Datasets

The preprocessed data are available at Baidu Netdisk (password: hgca) or Google Drive.

Please extract the zip file to folder data.

Example Usages

Before running the code，please make a directory named checkpoint.

• python run.py --cuda --dataset ACM --metapath-weight 0.4#0.6
• python run.py --cuda --dataset Yelp --metapath-weight 0.2#0.6#0.2
• python run.py --cuda --dataset DBLP --metapath-weight 0.1#0.1#0.8

Start From Zero (Using ACM dataset as an example)

1. Download data.zip and extract it to folder data (you can delete folders other than raw).
2. Run python preprocess_ACM.py (in data folder) to process raw data (generate a folder named ACM in data folder).
3. Run python sampling.py --dataset ACM (in preprocess folder) to sample nodes for batch training (generate a folder named indices in data/ACM folder).
4. Run python walk.py --dataset ACM (in preprocess folder) to generate sampled node sequence for training of metapath2vec (generate a file named walks_ACM.txt in preprocess folder).
5. Learn joint embeddings via metapath2vec and generate a file named metapath2vec_ACM_embeddings.txt in preprocess folder.
   • cd metapath2vec
   • ./metapath2vec -train ../preprocess/walks_ACM.txt -output ../preprocess/metapath2vec_ACM_embeddings -pp 0 -size 128 -window 4 -negative 10 -threads 32
6. Run python embedding.py --dataset ACM (in preprocess folder) to process metapath2vec_ACM_embeddings.txt (generate metapath2vec_emb_node.npy and metapath2vec_emb_word.npy in data/ACM folder).
7. Run python run.py --cuda --dataset ACM --metapath-weight 0.4#0.6

Please refer to the code for detailed parameters.

Citing

Dongxiao He, et al. "Analyzing Heterogeneous Network with Missing Attributes by Unsupervised Contrastive Learning," IEEE Trans. Neural Netw. Learn. Syst.