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.

中文翻译：

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网络社会网络中网络拓扑的推断和确认偏差

本文研究了具有代理传播状态的定向网络网络的拓扑推断，该网络具有意见传播动力学模型，该模型明确考虑了确认偏差。网络社交网络包括在社交级别的一组部分连接的代理定向网络，以及在网络层的一组信息源。给出了存在精确推理解的必要和充分条件。对于前面提到的偏差遵循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以及确认偏差模型参数的方法。没有确认偏见的特殊情况将详细分析。对于未知确认偏差模型的设置，需要一种近似网络拓扑的算法，提出了基于精确推论的方法。该算法可以准确地推断出从邻居到非跟随者的加权通信信息源。数值模拟证明了所提出方法在不同情况下的有效性。