复杂网络耗散性的熵起源
The entropic origin of disassortativity in complex networks
摘要：
为什么大多数经验网络，除了社会网络之外，一般都是度-度相关的，也就是说，是区分的？为了回答这个长期存在的问题，我们定义了一个一般类的度度相关网络，并获得相关的香农熵作为参数的函数。事实证明，最大熵通常不对应于不相关的网络，而是对应于非相关的（相关的）或非相关的（相关的）网络。更具体地说，对于高度异质（无标度）的网络，最大熵原理通常会导致离散性，从而为上述问题提供了一个简洁的解释。此外，通过比较在一些现实世界的网络中测量的相关性与那些产生最大熵的相同程度的序列，我们发现在各种情况下显着的协议。我们的方法提供了一个中立的模型，在没有进一步的知识，网络的演变，可以获得相关性的预期值。在经验观察偏离中性预测的情况下--就像在社交网络中发生的那样--人们可以推断出有特定的相关机制在起作用。
英文摘要：
Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated, i.e. disassortative? With a view to answering this long-standing question, we define a general class of degree-degree correlated networks and obtain the associated Shannon entropy as a function of parameters. It turns out that the maximum entropy does not typically correspond to uncorrelated networks, but to either assortative (correlated) or disassortative (anticorrelated) ones. More specifically, for highly heterogeneous (scale-free) networks, the maximum entropy principle usually leads to disassortativity, providing a parsimonious explanation to the question above. Furthermore, by comparing the correlations measured in some real-world networks with those yielding maximum entropy for the same degree sequence, we find a remarkable agreement in various cases. 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. In cases in which empirical observations deviate from the neutral predictions -- as happens in social networks -- one can then infer that there are specific correlating mechanisms at work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
