社交网络中的图论分布式推理
Graphic-theoretic distributed inference in social networks
摘要：
我们认为分布式推理在社交网络中的一个现象的兴趣演变在一个给定的社会互动图，称为\emph{social digraph}。对于推理，我们假设代理网络监控社交有向图中的某些节点，并且没有代理可以在其邻域内执行推理;代理必须依赖于智能体通信。本文的主要贡献包括：（一）一种新的构造的分布式估计和分布式观测的第一原则;（二）图论代理分类，建立的重要性和作用，每个代理对推理;（三）表征的必要条件，分类的基础上（二），在代理网络实现分布式观测。我们的结果是基于结构化系统理论，并适用于任何参数的选择，只要社会有向图保持固定的底层系统矩阵。换句话说，任何在结构不变的社会有向图上（线性）演化的社会现象都可以被考虑-我们将这种系统称为线性结构不变（LSI）。因此，上述贡献（i）-（iii）只需要社会有向图（拓扑）的知识，并且独立于社会现象。我们展示了结果对几个真实世界的社交网络的适用性，即僧侣之间的社会影响力，政治博客和书籍的网络，以及合著图。
英文摘要：
We consider distributed inference in social networks where a phenomenon of interest evolves over a given social interaction graph, referred to as the \emph{social digraph}. For inference, we assume that a network of agents monitors certain nodes in the social digraph and no agent may be able to perform inference within its neighborhood; the agents must rely on inter-agent communication. The key contributions of this paper include: (i) a novel construction of the distributed estimator and distributed observability from the first principles; (ii) a graph-theoretic agent classification that establishes the importance and role of each agent towards inference; (iii) characterizing the necessary conditions, based on the classification in (ii), on the agent network to achieve distributed observability. Our results are based on structured systems theory and are applicable to any parameter choice of the underlying system matrix as long as the social digraph remains fixed. In other words, any social phenomena that evolves (linearly) over a structure-invariant social digraph may be considered--we refer to such systems as Liner Structure-Invariant (LSI). The aforementioned contributions, (i)--(iii), thus, only require the knowledge of the social digraph (topology) and are independent of the social phenomena. We show the applicability of the results to several real-wold social networks, i.e. social influence among monks, networks of political blogs and books, and a co-authorship graph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
