Departamento de Electromagnetismo y Física de la Materia, and Institute Carlos I for Theoretical and Computational Physics, Facultad de Ciencias, University of Granada, 18071 Granada, Spain.
Phys Rev Lett. 2010 Mar 12;104(10):108702. doi: 10.1103/PhysRevLett.104.108702. Epub 2010 Mar 11.
Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated? To answer this long-standing question, we define the ensemble of correlated networks and obtain the associated Shannon entropy. Maximum entropy can correspond to either assortative (correlated) or disassortative (anticorrelated) configurations, but in the case of highly heterogeneous, scale-free networks a certain disassortativity is predicted--offering a parsimonious explanation for the question above. Our approach provides a neutral model from which, in the absence of further knowledge regarding network evolution, one can obtain the expected value of correlations. When empirical observations deviate from the neutral predictions--as happens for social networks--one can then infer that there are specific correlating mechanisms at work.
为什么大多数经验网络(社交网络是显著的例外)普遍具有度-度反相关性?为了解答这个长期存在的问题,我们定义了相关网络的集合,并获得了相应的香农熵。最大熵可以对应于正关联(相关)或负关联(反相关)的配置,但在高度异质的无标度网络的情况下,会预测到一定的负关联——这为上述问题提供了一个简洁的解释。我们的方法提供了一个中性模型,在没有关于网络演化的进一步知识的情况下,人们可以从中获得相关性的期望值。当经验观察与中性预测(如社交网络)发生偏差时,人们就可以推断出存在特定的关联机制在起作用。