On Manipulating Weight Predictions in Signed Weighted Networks

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 evading Fairness-Goodness Algorithm which is an edge weight prediction method for signed weighted networks developed by Kumar et al. in 2016. Among others, this method can be used for trust prediction in reputation systems. We study the theoretical underpinnings of this algorithm 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.