On Manipulating Weight Predictions in Signed Weighted Networks

TL;DR

This paper investigates the manipulability of a trust prediction method in signed weighted networks, finding it to be resistant to manipulation both theoretically and practically, unlike many other tools.

Contribution

It provides the first theoretical and practical analysis of the manipulability of FGA, a trust prediction method for signed weighted networks.

Findings

FGA is difficult to manipulate optimally.

Manipulating FGA in practice is also challenging.

FGA's robustness contrasts with other network analysis tools.

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.. 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.