2022/07/14 by Ernesto Estrada, Estrada, Ernesto, Gómez-Gardeñes, Jesús +1 · 1 citation
Computer Science · Neuroscience · Physics and Astronomy · #Complex Network Analysis Techniques #Data Analysis #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.2207.06813
openalex publication_date 2022/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Real-world networks are neither regular nor random, a fact elegantly explained by mechanisms such as the Watts-Strogatz or the Barabasi-Albert models, among others. Both mechanisms naturally create shortcuts and hubs, which while enhancing network's connectivity, also might yield several undesired navigational effects: they tend to be overused during geodesic navigational processes -- making the networks fragile -- and provide suboptimal routes for diffusive-like navigation. Why, then, networks with complex topologies are ubiquitous? Here we unveil that these models also entropically generate network bypasses: alternative routes to shortest paths which are topologically longer but easier to navigate. We develop a mathematical theory that elucidates the emergence and consolidation of network bypasses and measure their navigability gain. We apply our theory to a wide range of real-world networks and find that they sustain complexity by different amounts of network bypasses. At the top of this complexity ranking we found the human brain, which points out the importance of these results to understand the plasticity of complex systems.