Gounaris Georgios, Katifori Eleni
Department of Physics and Astronomy, <a href="https://ror.org/00b30xv10">University of Pennsylvania</a>, Philadelphia, Pennsylvania 19104, USA.
Center for Computational Biology, <a href="https://ror.org/00sekdz59">Flatiron Institute</a>, New York, New York 10010, USA.
Phys Rev Lett. 2024 Aug 9;133(6):067401. doi: 10.1103/PhysRevLett.133.067401.
In stochastic exploration of geometrically embedded graphs, intuition suggests that providing a shortcut between a pair of nodes reduces the mean first passage time of the entire graph. Counterintuitively, we find a Braess's paradox analog. For regular diffusion, shortcuts can worsen the overall search efficiency of the network, although they bridge topologically distant nodes. We propose an optimization scheme under which each edge adapts its conductivity to minimize the graph's search time. The optimization reveals a relationship between the structure and diffusion exponent and a crossover from dense to sparse graphs as the exponent increases.
在对几何嵌入图的随机探索中,直觉表明在一对节点之间提供一条捷径会减少整个图的平均首次通过时间。与直觉相反,我们发现了一个类似布雷斯悖论的现象。对于常规扩散,捷径会降低网络的整体搜索效率,尽管它们连接了拓扑距离较远的节点。我们提出了一种优化方案,在该方案下每条边会调整其传导率以最小化图的搜索时间。该优化揭示了结构与扩散指数之间的关系,以及随着指数增加从密集图到稀疏图的转变。