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.
Emergent Behaviors over Signed Random Dynamical Networks: Relative-State-Flipping Model
我们研究了一组动态演化的符号随机网络中相互作用的节点之间出现的渐近动力学模式，其中正链接进行标准的共识和负链接诱导相对状态翻转。一系列确定性符号图定义了独立发生的潜在节点交互。每个节点从其正邻居接收与标准共识算法一致的正推荐，并且从其负邻居接收由相对状态翻转定义的负推荐。每个节点收到这些推荐后，对每个推荐赋予确定的权重，然后通过由两个Bernoulli随机变量定义的随机关注度，将这些加权推荐编码到节点的状态更新中。我们建立了一些条件，几乎必然收敛和发散的节点状态。我们还提出了一个条件，几乎必然的状态聚类的基本弱平衡图，与几个鞅收敛引理的帮助下。当前的相对状态翻转模型与Altafini 2013和Shi et al. 2014中考虑的状态翻转模型在确定性权重和随机关注对节点状态演化的影响方面存在一些根本差异。
IGA-LWP: An Iterative Gradient-based Adversarial Attack for Link Weight Prediction
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）
