Learning Structural Features of Nodes in Large-Scale Networks for Link Prediction
Abstract
We present an algorithm (LsNet2Vec) that, given a large-scale network (millions of nodes), embeds the structural features of node into a lower and fixed dimensions of vector in the set of real numbers. We experiment and evaluate our proposed approach with twelve datasets collected from SNAP. Results show that our model performs comparably with state-of-the-art methods, such as Katz method and Random Walk Restart method, in various experiment settings.
Cite
Text
Zhiyuli et al. "Learning Structural Features of Nodes in Large-Scale Networks for Link Prediction." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9919Markdown
[Zhiyuli et al. "Learning Structural Features of Nodes in Large-Scale Networks for Link Prediction." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/zhiyuli2016aaai-learning/) doi:10.1609/AAAI.V30I1.9919BibTeX
@inproceedings{zhiyuli2016aaai-learning,
title = {{Learning Structural Features of Nodes in Large-Scale Networks for Link Prediction}},
author = {Zhiyuli, Aakas and Liang, Xun and Zhou, Xiaoping},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {4286-4288},
doi = {10.1609/AAAI.V30I1.9919},
url = {https://mlanthology.org/aaai/2016/zhiyuli2016aaai-learning/}
}