Controllability of Complex Networks

复杂网络的可控性

• 批准号: RGPIN-2017-06413
• 负责人: White, Tony
• 金额: $ 1.46万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750396
• 关键词: Controllability; Complex; Networks

A measure of our understanding of naturally occurring or artificial systems can be observed in our ability to control them. One abstract representation of such systems that is increasingly been used for analysis is that of a complex network. Complex networks are found in many domains (e.g., Biology and the Web) and the control of these networks is a research topic that has started to draw increasing research attention. While engineering has provided tools for developing control systems which can force a system to follow a particular state trajectory, no framework exists for control of complex, possibly self-organizing, networks. The main objective of this proposal is to identify conditions under which control is possible in complex networks and develop control solutions for exemplar problems within the domain of network controllability. The novelty of this research is in its exploration of the solution to the interacting problems of deciding which nodes in the network to control and which signals to inject into the selected network nodes in order to achieve a particular time dependent objective.Controllability of complex networks [1] led to our formalization of the Network Control Problem (NCP) [2]. Early indications are that controllability is possible in a wide range of scenarios. Simple neural network controllers have been developed; we propose that deep learning and reinforcement learning should be evaluated on exemplar NCP instances drawn from the taxonomy outlined in [2]. Of particular interest is distribution-based control (DbC), a class of network control problem that attempts to maintain the distribution of network node states close to a target distribution; e.g., preventing social network opinions from becoming too extreme. Analytically, we propose the development of techniques similar to landscape analysis to assess DbC difficulty. By examining node influence and various network motifs such as identifiable communities we expect that a deeper understanding of structures of importance will be forthcoming. Finally, we see DbC as an important approach to catastrophe or bubble avoidance. Here, a market place is backed by a social network of agents whose actions are interdependent. Our objective would be to examine the effectiveness of DbC as a market place control mechanism.To summarize, this proposal will make contributions in the area of complex network controllability; most notably in the area of distribution-based control. The principal investigator has already contributed to the NCP research community [2] and sees great potential in the application of the proposed research to market control.[1] Y.-Y. Liu, J.-J. Slotine, and A.-L. Barabsi, “Controllability of complex networks,” Nature, vol. 473, no. 7346, pp. 167173, 2011.[2] A. Runka and T. White, “Towards Intelligent Control of Influence Diffusion in Social Networks,” Social Network Analysis and Mining, vol. 5, no. 1, 2015.

可以在控制它们的能力中观察到我们对自然发生或人造系统的理解的衡量标准。越来越多地用于分析的系统的一种抽象表示是复杂的网络。在许多领域（例如生物学和网络）中发现了复杂的网络，对这些网络的控制是一个研究主题，已开始引起越来越多的研究关注。尽管工程为开发控制系统提供了工具，该工具可以迫使系统遵循特定的状态轨迹，但不存在控制复杂，可能的自组织网络的框架。该提案的主要目的是确定在复杂网络中可能进行控制的条件，并为网络可控性领域内的示例性问题开发控制解决方案。这项研究的新颖性在于它探索了解决网络中哪些节点的相互作用问题的解决方案，以及哪些信号将其注入选定的网络节点以实现特定时间依赖的目标。早期的迹象表明，在各种场景中可以进行可控性。已经开发了简单的神经网络控制器；我们建议，应根据[2]中概述的分类法所绘制的示例NCP实例评估深度学习和强化学习。特别感兴趣的是基于分布的控制（DBC），这是一类网络控制问题，试图维持接近目标分布的网络节点状态的分布；例如，防止社交网络意见变得太极端。从分析上，我们提出的技术开发与景观分析相似，以评估DBC难度。通过检查节点影响力和各种网络图案，例如可识别的社区，我们期望将对重要性结构有更深入的了解。最后，我们将DBC视为避免灾难或气泡的重要方法。在这里，一个市场得到了一个相互依存的代理商的社交网络的支持。我们的目标是研究DBC作为市场控制机制的有效性。总而言之，该提案将在复杂的网络可控性方面做出贡献。最值得注意的是在基于分配的控制领域。首席研究者已经为NCP研究界做出了贡献[2]，并认为拟议的研究在市场控制中的应用中具有巨大的潜力。[1] Y.-Y.刘J.-J。 slotine和A.-L。 Barabsi，“复杂网络的可控制性”，《自然》，第1卷。 473，没有。 7346，第167173页，2011年。[2] A. Runka和T. White，“智能控制社交网络中影响力扩散”，《社交网络分析与采矿》，第1卷。 5，不。 1，2015。