论文标题
多路复用网络上的历史依赖性渗透
History-dependent percolation on multiplex networks
论文作者
论文摘要
互连系统的结构及其对系统动力学的影响是一个备受研究的跨学科主题。尽管在不同的模型中发现了各种关键现象，但仍缺乏关于不同渗透转变之间联系的研究。在这里，我们提出了一个统一的框架，以研究相互作用网络上渗透过程不连续过渡的起源。该模型以当前渗透取决于先前状态的结果而在世代相传，因此与历史有关。理论分析和蒙特卡洛模拟都表明，过渡的性质在有限世代保持不变，但对于无限发电却突然变化。我们使用大脑功能相关性和形态相似性数据来表明我们的模型还提供了一种探索网络结构的通用方法，并可以为许多实际应用做出贡献，例如检测人脑网络的异常结构。
The structure of interconnected systems and its impact on the system dynamics is a much-studied cross-disciplinary topic. Although various critical phenomena have been found in different models, the study on the connections between different percolation transitions is still lacking. Here we propose a unified framework to study the origins of the discontinuous transitions of the percolation process on interacting networks. The model evolves in generations with the result of the present percolation depending on the previous state and thus is history-dependent. Both theoretical analysis and Monte Carlo simulations reveal that the nature of the transition remains the same at finite generations but exhibits an abrupt change for the infinite generation. We use brain functional correlation and morphological similarity data to show that our model also provides a general method to explore the network structure and can contribute to many practical applications, such as detecting the abnormal structures of human brain networks.
