# On Manipulating Weight Predictions in Signed Weighted Networks

arXiv: https://arxiv.org/abs/2302.02687 · HTML: https://mlsanity.com/paper/2302.02687/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [Graph Spring Neural ODEs for Link Sign Prediction](https://mlsanity.com/paper/2412.12916/) (2024)

## Abstract (arXiv)

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.. 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.