Cooperative Association for Internet Data Analysis (CAIDA), University of California , San Diego (UCSD), La Jolla, CA 92093, USA.
Sci Rep. 2012;2:793. doi: 10.1038/srep00793. Epub 2012 Nov 16.
Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.
预测和控制复杂网络的动态是网络科学的一个核心问题。不同真实网络的结构和动态相似性表明,一些普遍规律可能可以准确地描述这些网络的动态,尽管这些规律的性质和共同起源仍然难以捉摸。在这里,我们表明,代表我们加速宇宙中时空大尺度结构的因果网络是一个具有强聚类的幂律图,类似于许多复杂网络,如互联网、社交或生物网络。我们证明这种结构相似性是复杂网络和因果网络的大尺度增长动态之间渐近等价的结果。这种等价性表明,出乎意料地相似的规律控制着复杂网络和宇宙时空中的动态,这对网络科学和宇宙学具有影响。