Randomized opinion dynamics over networks: influence estimation from partial observations
Randomized opinion dynamics over networks: influence estimation from partial observations
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DOI：
10.1109/cdc.2018.8619770
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Chiara Ravazzi;Sarah Hojjatinia;C. Lagoa;F. Dabbene
中科院分区：
文献类型：
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作者：
Chiara Ravazzi;Sarah Hojjatinia;C. Lagoa;F. Dabbene
在本文中，我们提出了一种在稀疏社交网络中估计影响力矩阵的技术，其中 n 个个体以八卦方式进行通信。在每一步中，社会参与者的随机子集都是活跃的，并与随机选择的邻居进行交互。这些观点根据弗里德金和约翰森机制演变，其中个体将他们的信念更新为他们当前信念、与他们互动的代理人的信念以及他们最初的信念或偏见的凸组合。利用向量自回归过程估计的最新结果，我们从交互的部分观察开始重建社交网络拓扑和互连的强度，从而消除了有限水平技术的主要缺点之一。该方法的有效性在随机生成网络上得到了证明。
In this paper, we propose a technique for the estimation of the influence matrix in a sparse social network, in which n individual communicate in a gossip way. At each step, a random subset of the social actors is active and interacts with randomly chosen neighbors. The opinions evolve according to a Friedkin and Johnsen mechanism, in which the individuals updates their belief to a convex combination of their current belief, the belief of the agents they interact with, and their initial belief, or prejudice. Leveraging recent results of estimation of vector autoregressive processes, we reconstruct the social network topology and the strength of the interconnections starting from partial observations of the interactions, thus removing one of the main drawbacks of finite horizon techniques. The effectiveness of the proposed method is shown on randomly generation network.