Evolutionary Optimisation of Networks towards Complexity

与ErdöS和Renyi介绍的随机图相比，网络科学提供了在交互拓扑中引入更复杂的系统方法。在哪些生物系统中，更高的复杂性会带来更好的复原力或其他进化益处？这种观点能为改进技术和社会进程提供洞察力吗？该项目采用了一种新的方法，利用网络复杂性度量来生成复杂性不断增加的网络集合，随后调查网络复杂性如何影响所产生的网络动态的复杂性。这种方法的动机是一个常见的隐含假设，即复杂结构对自适应系统有利，目的是将有益的复杂网络拓扑转化为特定的应用。预计增加的复杂性可能会产生有益的影响(就像在自然界中进化的网络一样)，或者可能会阻碍功能(预计会出现在物流或生产网络中)，在这些功能中，弹性和适应性的好处是以更复杂结构的组织成本为代价的。因此，该项目包括两个面向应用的动态建模研究，其中预期更高的复杂性会提高网络性能，但也会调查可能挑战此类范例的通用性的情况。

Network science provides systematic approaches of introducing more complexity in the interaction topology, compared to random graphs as introduced by Erdös and Renyi. In which biological systems does a higher complexity result in improved resilience or other evolutionary benefits? Can this perspective provide insight for improving processes in technology and society? This project takes the novel approach of utilising network complexity measures to generate ensembles of networks of increasing complexity, followed up by an investigation into how network complexity influences the complexity of the resulting network dynamics. This approach is motivated by a common implicit hypothesis that complex structures are beneficial for adaptive systems, and aims to translate back beneficial complex network topologies into specific applications. It is expected that increased complexity can have a beneficial impact (as in networks evolved in nature), or might impede functionality (expected to appear in logistic or production networks) where benefits of resilience and adaptivity come at organisational costs of a more complex structure. The project therefore includes two application-oriented dynamical modelling studies where increased network performance from higher complexity is expected, but also investigates cases which might challenge the generality of such a paradigm.