不好意思，还号称做过一点点网络研究的，结果到今天才知道原因：

 

a normal distribution, which curves sharply on a log-log plot, such that the probability of a node having a degree greater than a certain “cutoff” value is effectively zero. A cutoff therefore implies a
characteristic scale for the degree distribution of the network, and because a power law
degree distribution lacks any such cutoff value, it is often called a scale-free distribution.

 

A Study of NK Landscapes’ Basins and Local Optima Networks

 

A second important aspect in the study of networks has been the realization that in many real-world networks, the distribution of the number of neighbours (the degree distribution) is typically right-skewed with a "heavy tail", meaning that most of the nodes have less-than-average degree whilst a small fractions of hubs have
a large number of connections. These qualitative description can be described mathematically by a power-law [1], which has the asymptotic form p(k)  k−. This means that the probability of a randomly chosen point having a degree k decays like a power of k, where the exponent  (typically in the range [2, 3]) determines
the rate of decay. A distinguishing feature of power-law distributions is that when plotted on a double logarithmic scale, a powerlaw appears as a straight line with negative slope . This behavior contrasts with a normal distribution which would curve sharply on a log-log plot, such that the probability of a node having a degree greater than a certain "cutoff" value is nearly zero. The mean would then trivially represent a characteristic scale for the network degree distribution. Since networks with power-low degree distribution
lack any such cutoff value, at least in theory, they are often called scale-free networks [20]. Examples of such scale-free networks are the world-wide-web, the internet, scientific collaboration and citation networks, and biochemical networks.

在网络研究第二个重要方面是在很多现实世界的网络结构中，节点邻居数量的分布通常是有着厚重尾巴的右倾斜分布，这意味着大多数节点的度少于平均数，而很少一部分的节点有很大的节点。这种数量描述可以用数学中的幂律来描述，这种幂律有着渐近线是。。。这意味着随机选择一个度为k的节点的概率随着幂k而减少，指数通常在2-3之间，指数大小决定着衰减的速度。幂律分布的典型特征是如果把这些点房子双Log坐标轴上的话，幂律分布就是一条斜率为负数的直线。这种性质和其整台分布相对。正态分布要是画在双LOG坐标轴上的话，会急剧弯曲。这种正态分布，选择一个比一个截止值更大的度的概率近乎为零。平均值就明显地代表着网络度分布的一个标度。而幂律分布的网络没有这样一个截止值，至少在理论上缺乏这么一个截止值。符合幂律分布的网络就要无标度网络。无标度网络有万维网，internet， 科学合作网和引文网，，生物化学网等。