Rethinking the Link Prediction Problem in Signed Social Networks
Abstract
We rethink the link prediction problem in signed social networks by also considering "no-relation" as a future status of a node pair, rather than simply distinguishing positive and negative links proposed in the literature. To understand the underlying mechanism of link formation in signed networks, we propose a feature framework on the basis of a thorough exploration of potential features for the newly identified problem. Grounded on the framework, we also design a trinary classification model, and experimental results show that our method outperforms the state-of-the-art approaches.
Cite
Text
Li et al. "Rethinking the Link Prediction Problem in Signed Social Networks." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.11096Markdown
[Li et al. "Rethinking the Link Prediction Problem in Signed Social Networks." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/li2017aaai-rethinking/) doi:10.1609/AAAI.V31I1.11096BibTeX
@inproceedings{li2017aaai-rethinking,
title = {{Rethinking the Link Prediction Problem in Signed Social Networks}},
author = {Li, Xiaoming and Fang, Hui and Zhang, Jie},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {4955-4956},
doi = {10.1609/AAAI.V31I1.11096},
url = {https://mlanthology.org/aaai/2017/li2017aaai-rethinking/}
}