论文标题
增强互连系统中的运输特性而不改变其结构
Enhancing transport properties in interconnected systems without altering their structure
论文作者
论文摘要
复杂系统的单位（例如大脑中的神经元或社会中的个体）必须有效地通信以正常运行：单位之间不同类型的关系的共存，需要多层的代表，其中将类型视为由层所编码的网络，在它们之间的信息交换质量中起着重要作用。在更改此类系统的结构时（例如，通过物理添加（或卸下）单元，连接或层）可能是昂贵的，以减少多层系统跨越多种扩散途径的数量的方式耦合了层层的动态，但可以潜在地加速整体信息流。为此，我们引入了一个功能降低的框架，该框架使我们能够通过将层将层耦合而不是结构来增强多层系统中的传输现象。从数学上讲，最佳配置是通过最大化系统熵与自由和非相互作用层极限的偏差获得的。我们的结果提供了一个透明的程序，以减少扩散时间并优化经验多层系统中的非压缩搜索过程，而无需更改基础结构的成本。
Units of complex systems -- such as neurons in the brain or individuals in societies -- must communicate efficiently to function properly: e.g., allowing electrochemical signals to travel quickly among functionally connected neuronal areas in the human brain, or allowing for fast navigation of humans and goods in complex transportation landscapes. The coexistence of different types of relationships among the units, entailing a multilayer represention in which types are considered as networks encoded by layers, plays an important role in the quality of information exchange among them. While altering the structure of such systems -- e.g., by physically adding (or removing) units, connections or layers -- might be costly, coupling the dynamics of subset(s) of layers in a way that reduces the number of redundant diffusion pathways across the multilayer system, can potentially accelerate the overall information flow. To this aim, we introduce a framework for functional reducibility which allow us to enhance transport phenomena in multilayer systems by coupling layers together with respect to dynamics rather than structure. Mathematically, the optimal configuration is obtained by maximizing the deviation of system's entropy from the limit of free and non-interacting layers. Our results provide a transparent procedure to reduce diffusion time and optimize non-compact search processes in empirical multilayer systems, without the cost of altering the underlying structure.
