On Manipulating Weight Predictions in Signed Weighted Networks
Abstract
Adversarial social network analysis studies how graphs can be rewired or otherwise manipulated to evade social network analysis tools. While there is ample literature on manipulating simple networks, more sophisticated network types are much less understood in this respect. In this paper, we focus on the problem of evading FGA---an edge weight prediction method for signed weighted networks by Kumar et al. 2016. Among others, this method can be used for trust prediction in reputation systems. We study the theoretical underpinnings of FGA and its computational properties in terms of manipulability. Our positive finding is that, unlike many other tools, this measure is not only difficult to manipulate optimally, but also it can be difficult to manipulate in practice.
Cite
Text
Lizurej et al. "On Manipulating Weight Predictions in Signed Weighted Networks." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I4.25652Markdown
[Lizurej et al. "On Manipulating Weight Predictions in Signed Weighted Networks." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/lizurej2023aaai-manipulating/) doi:10.1609/AAAI.V37I4.25652BibTeX
@inproceedings{lizurej2023aaai-manipulating,
title = {{On Manipulating Weight Predictions in Signed Weighted Networks}},
author = {Lizurej, Tomasz and Michalak, Tomasz and Dziembowski, Stefan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {5222-5229},
doi = {10.1609/AAAI.V37I4.25652},
url = {https://mlanthology.org/aaai/2023/lizurej2023aaai-manipulating/}
}