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Neurocomputing; Available online 1 June 2020; In Press, Journal Pre-proof version
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@ -27,14 +27,16 @@ PNE methods:
[others](https://github.com/thunlp/OpenNE)
<br> All methods in this framework are **unsupervised**, and so do not require any label during embedding phase.
For more details of each method, please have a look at our paper https://arxiv.org/abs/1811.11728. And if you find ABRW (namely RoSANE in the paper) or this framework is useful for your research, please consider citing it.
For more details of each method, please have a look at our paper https://doi.org/10.1016/j.neucom.2020.05.080. And if you find [ABRW (namely RoSANE in the paper) or this framework](https://doi.org/10.1016/j.neucom.2020.05.080) is useful for your research, please consider citing it.
```
@article{hou2020RoSANE,
title={RoSANE: Robust and Scalable Attributed Network Embedding for Sparse Networks},
author={Hou, Chengbin and He, Shan and Tang, Ke},
journal={Neurocomputing},
year={2020},
publisher={Elsevier}
publisher={Elsevier},
url={https://doi.org/10.1016/j.neucom.2020.05.080},
doi={10.1016/j.neucom.2020.05.080},
}
```