In this article, we address the problem of inferring direct influences in social networks from partial samples of a class of opinion dynamics. The interest is motivated by the study of several complex systems arising in social sciences, where a population of agents interacts according to a communication graph. These dynamics over networks often exhibit an oscillatory behavior, given the stochastic effects or the random nature of the local interactions process. Inspired by recent results on estimation of vector autoregressive processes, we propose a method to estimate the social network topology and the strength of the interconnections starting from partial observations of the interactions, when the whole sample path cannot be observed due to limitations of the observation process. Besides the design of the method, our main contributions include a rigorous proof of the convergence of the proposed estimators and the evaluation of the performance in terms of complexity and number of sample. Extensive simulations on randomly generated networks show the effectiveness of the proposed technique.

中文翻译：

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网络上的遍历观点动态：学习部分观察的影响


在本文中，我们解决了从一类舆论动态的部分样本推断社交网络中的直接影响的问题。人们的兴趣源于对社会科学中出现的几个复杂系统的研究，其中一群代理根据通信图进行交互。考虑到随机效应或局部交互过程的随机性质，网络上的这些动态通常表现出振荡行为。受向量自回归过程估计最新结果的启发，当由于观察过程的限制而无法观察到整个样本路径时，我们提出了一种从交互的部分观察开始估计社交网络拓扑和互连强度的方法。除了方法的设计之外，我们的主要贡献包括对所提出的估计器的收敛性的严格证明以及在复杂性和样本数量方面的性能评估。对随机生成网络的广泛模拟显示了所提出技术的有效性。