The emergent properties of information processing in Bow Tie complex networks

蝴蝶结复杂网络中信息处理的新兴特性

• 批准号: 2274614
• 金额: --
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2274614
• 关键词: emergent; properties; information; processing; Bow

This project will work to develop novel measures and methods for identifying nodes in complex networks that will dictate the final (steady) states of the network, with a focus on (but not limited to) biological networks as a target network type. Complex networks are comprised primarily of two types of representation, discrete (Boolean) and continuous (Ordinary differential equation) networks. It has been noted that many biological systems display bow tie architectures. Such that there are three distinct layers in the network; an input component, a strongly connected core and an output component. Literature hypothesises that information processing is an emergent feature of bow tie networks. Currently methods utilise either dynamical or structural information to identify a set of control nodes of a given network. I will develop a novel method that will use both structural and dynamical measures via the implementation of a genetic algorithm to identify a set of nodes that will control a complex network. Particularly targeting the states of output nodes. Research will focus on the development of an algorithm decomposing complex networks utilizing dynamical features of a network such as maximum entropy and structural features i.e centrality, criticality or connectedness measures.A set of motifs (such as feedback loops, feed forward loops etc), will be established to further understand complex features of network structures. Motifs can be described as micro level effects with macro effects on networks. Particular attention will be paid to the strongly connected core of a network, as more complex motifs are likely to appear due to the higher edge density. Understanding the effects of specific motifs on the macro behaviour of the network may allow for new methods to dictate final states.In one sentence my research will develop novel measures and methods that utilise both structural and dynamical information to find nodes in complex networks that can dictate steady states. This will be achieved by developing understanding of the underlying motifs and using artificial intelligence techniques such as evolutionary algorithms to identify target nodes.

该项目将致力于开发新的措施和方法来识别复杂网络中的节点，这些节点将决定网络的最终（稳定）状态，重点是（但不限于）生物网络作为目标网络类型。复杂网络主要由两种类型的表示组成，离散（布尔）和连续（常微分方程）网络。已经注意到，许多生物系统显示领结结构。这样在网络中就有三个不同的层；一个输入组件，一个强连接核心和一个输出组件。文献假设信息处理是领结网络的一个突现特征。目前的方法利用动态或结构信息来识别给定网络的一组控制节点。我将开发一种新的方法，通过实现遗传算法来使用结构和动态措施来识别一组控制复杂网络的节点。特别是针对输出节点的状态。研究将集中于开发一种算法，利用网络的动态特征（如最大熵）和结构特征（即中心性、临界性或连通性度量）分解复杂网络。将建立一组基序（如反馈回路、前馈回路等），以进一步理解网络结构的复杂特征。母题可以被描述为具有网络宏观效应的微观效应。将特别注意网络的强连接核心，因为由于更高的边缘密度，可能会出现更复杂的图案。了解特定基序对神经网络宏观行为的影响，可能会有新的方法来决定最终状态。简而言之，我的研究将开发新的措施和方法，利用结构和动态信息来寻找复杂网络中可以决定稳定状态的节点。这将通过发展对潜在主题的理解和使用人工智能技术（如进化算法）来识别目标节点来实现。