This repository contains the Python implementation for SCAN. Further details about SCAN can be found in our paper:
Zaiqiao Meng, Shangsong Liang, Jinyuan Fang, Teng Xiao. Semi-supervisedly Co-embedding Attributed Networks. (NeurIPS 2019)
- A pytorch implementation can be found here
SCAN is a semi-supervised co-embedding model for attributed networks based on the generalized SVAE for heterogeneous data, which collaboratively learns low-dimensional vector representations of both nodes and attributes for partially labelled attributed networks semi-supervisedly. The node and attribute embeddings obtained in a unified manner by our SCAN can benefit not only for capturing the proximities between nodes but also the affinities between nodes and attributes. Moreover, our model also trains a discriminative network to learn the label predictive distribution of nodes.
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- TensorFlow (1.0 or later)
- python 3.6
- scikit-learn
- scipy
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python main.py
If you want to use our codes and datasets in your research, please cite:
@inproceedings{meng2019scan,
title={Semi-supervisedly Co-embedding Attributed Networks},
author={Meng, Zaiqiao and Liang, Shangsong and Fang, Jinyuan and Xiao, Teng},
booktitle={Advances in neural information processing systems},
year={2019}
}