期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2019
卷号：116
期号：34
页码：16666-16668
DOI：10.1073/pnas.1911715116
出版社：The National Academy of Sciences of the United States of America
摘要：Network analysis deals with the mathematical characterization of structure and topology of networks which are envisaged as sets of “nodes” connected by “edges. Its concepts do not depend on the physical nature of the entities that constitute a network—be it neurons connected by synapses, cities connected by highways, or humans connected by acquaintance relationships. Despite their general and abstract nature, network analytic concepts are often useful tools to analyze physical processes on networks, which may strongly depend on structural properties of the network and their evolution. Berthier et al. (1) demonstrate this by forecasting failure locations in load-carrying lattice structures based on so-called geodesic edge betweenness centrality (GEBC). Edge betweenness centrality ranks the importance of edges based on the fraction of shortest paths between any 2 points on a network that traverse a given edge (2), and Berthier et al. (1) demonstrate that a large GEBC value serves as a good predictor for failure locations in disordered beam networks subjected to tensile or compressive loading: They performed an experiment where they successively loaded a network and recorded the sequence of beam failures, while analyzing the concomitant evolution of the centrality pattern. They show that high GEBC correlates strongly with beam failure and achieves prediction scores that are comparable to those obtained from a simple physical model of load redistribution. To understand how GEBC might relate to failure processes, let us first consider an idealized road network where traffic occurs between any 2 nodes with equal probability, and where drivers always take the shortest route. In this case it is evident that GEBC directly determines the traffic load on any stretch of road, and therefore the likelihood for such a.
