On Inference of Network Topology and Confirmation Bias in Cyber-Social Networks
On Inference of Network Topology and Confirmation Bias in Cyber-Social Networks
复制标题
DOI：
10.1109/tsipn.2020.3015283
复制
发表时间：
2019-08
影响因子：
3.2
通讯作者：
Y. Mao;E. Akyol
中科院分区：
文献类型：
--
作者：
Y. Mao;E. Akyol
本文利用观点传播动力学模型研究了基于主体状态的有向网络-社会网络的拓扑推理，该模型明确考虑了确认偏差。网络-社交网络包括社交层的一组部分连接的有向代理网络和网络层的一组信息源。给出了精确推理解存在的充要条件。对于前面提到的偏差服从分段线性模型的情况，提出了一种在可能的情况下精确推断整个网络拓扑结构和确认偏差模型参数的方法。对无确认偏差的特殊情况进行了详细的分析。对于确认偏差模型未知的情况，提出了一种建立在精确推理方法基础上的近似网络拓扑算法。该算法能够准确地推断出信息源邻居到非跟随者的加权通信。数值仿真验证了所提方法在不同场景下的有效性。
This article studies topology inference, from agent states, of a directed cyber-social network with opinion spreading dynamics model that explicitly takes confirmation bias into account. The cyber-social network comprises a set of partially connected directed network of agents at the social level, and a set of information sources at the cyber layer. The necessary and sufficient conditions for the existence of exact inference solution are characterized. A method for exact inference, when it is possible, of entire network topology as well as confirmation bias model parameters is proposed for the case where the bias mentioned earlier follows a piece-wise linear model. The particular case of no confirmation bias is analyzed in detail. For the setting where the model of confirmation bias is unknown, an algorithm that approximates the network topology, building on the exact inference method, is presented. This algorithm can exactly infer the weighted communication from the neighbors to the non-followers of information sources. Numerical simulations demonstrate the effectiveness of the proposed methods for different scenarios.