论文标题
分散网络中意见融合的集中化问题
Centralization Problem for Opinion Convergence in Decentralized Networks
论文作者
论文摘要
本文旨在提供有关权力下放之间的相互作用的新观点（多代理系统的普遍特征）和集中化，即强加核心控制以实现系统级目标的任务。特别是，在网络意见动态模型的背景下，本文提出并讨论了集中化的框架。更确切地说，一个分散的网络由自主代理人及其社会结构组成，其社会结构是未知和动态的。集中化是一个任命网络代理的过程，以充当访问单位，这些单位提供信息并对当地环境发挥影响力。我们讨论了对意见动力学模型的集中化，旨在使用最小访问单位数量来强制汇聚。我们表明，集中化过程的关键在于选择访问单元，以便它们形成主导集。然后，我们在新的本地算法框架下提出算法，即耕种，以完成此任务。为了验证我们的算法，我们对现实世界和合成网络进行系统实验，并验证我们的算法是否优于基准。
This paper aims to provide a new perspective on the interplay between decentralization -- a prevalent character of multi-agent systems -- and centralization, i.e., the task of imposing central control to meet system-level goals. In particular, in the context of networked opinion dynamic model, the paper proposes and discusses a framework for centralization. More precisely, a decentralized network consists of autonomous agents and their social structure that is unknown and dynamic. Centralization is a process of appointing agents in the network to act as access units who provide information and exert influence over their local surroundings. We discuss centralization for the DeGroot model of opinion dynamics, aiming to enforce opinion convergence using the minimum number of access units. We show that the key to the centralization process lies in selecting access units so that they form a dominating set. We then propose algorithms under a new local algorithmic framework, namely prowling, to accomplish this task. To validate our algorithm, we perform systematic experiments over both real-world and synthetic networks and verify that our algorithm outperforms benchmarks.
