This repository provides a reference implementation of HNECV as described in the paper:
HNECV: Heterogeneous Network Embedding via Cloud model and Variational inference.
Ming Yuan, LiuQun, Guoyin Wang, Yike Guo.
CAAI International Conference on Artificial Intelligence. 2021.
The paper has been accepted by CICAI, available at here and SpringLink
The processed data used in the paper are available at:
You need to perform the following steps for the downloaded file:
- Move
SingleDBLP.mat
to theHNECV/dataset/DBLP/
- Move
SingleAminer.mat
to theHNECV/dataset/AMiner/
- Move
SingleYelp.mat
to theHNECV/dataset/Yelp/
If you only want to train the model, you need to specify a certain data set, such as dblp, aminer, yelp
python pytorch_HNECV.py --dataset dblp
If you want to understand all the processes of the model, you can execute the following command
python pipline.py --dataset dblp
noted: You can adjust the hyperparameters in pytorch_HNECV.py or pipeline.py according to your needs
- Python ≥ 3.6
- PyTorch ≥ 1.7.1
- scipy ≥ 1.5.2
- scikit-learn ≥ 0.21.3
- tqdm ≥ 4.31.1
- numpy
- pandas
- matplotlib
Your input file must be a adjacency matrix, which can be a mat file or other compressed format
If you only have the edgelist file, you need to follow the preprocessing method in pipline.py, and rewrite the corresponding semantic random walk code.
noted: If you run pytorch_HNECV.py directly, You need at least the label file of the node, like the initial file in the
dataset/DBLP/reindex_dblp/
folder
If HNECV is useful for your research, please cite the following paper:
@inproceedings{DBLP:conf/cicai/YuanLWG21,
author = {Ming Yuan and
Qun Liu and
Guoyin Wang and
Yike Guo},
editor = {Lu Fang and
Yiran Chen and
Guangtao Zhai and
Z. Jane Wang and
Ruiping Wang and
Weisheng Dong},
title = {{HNECV:} Heterogeneous Network Embedding via Cloud Model and Variational
Inference},
booktitle = {Artificial Intelligence - First {CAAI} International Conference, {CICAI}
2021, Hangzhou, China, June 5-6, 2021, Proceedings, Part {I}},
series = {Lecture Notes in Computer Science},
volume = {13069},
pages = {747--758},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-93046-2\_63},
doi = {10.1007/978-3-030-93046-2\_63},
timestamp = {Fri, 14 Jan 2022 09:56:35 +0100},
biburl = {https://dblp.org/rec/conf/cicai/YuanLWG21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}