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A random graph is obtained by starting with a set of n isolated vertices and adding successive edges between them at random. The aim of the study in this field is to determine at what stage a particular property of the graph is likely to arise. Different random graph models produce different probability distributions on graphs. Most commonly studied is the one proposed by Edgar Gilbert, denoted G(n,p), in which every possible edge occurs independently with probability 0 < p < 1. The probability of obtaining any one particular random graph with m edges is <math>p^m (1-p)^{N-m}</math> with the notation <math>N = \tbinom{n}{2}</math>.

A random graph is obtained by starting with a set of n isolated vertices and adding successive edges between them at random. The aim of the study in this field is to determine at what stage a particular property of the graph is likely to arise. Different random graph models produce different probability distributions on graphs. Most commonly studied is the one proposed by Edgar Gilbert, denoted G(n,p), in which every possible edge occurs independently with probability 0 < p < 1. The probability of obtaining any one particular random graph with m edges is <math>p^m (1-p)^{N-m}</math> with the notation <math>N = \tbinom{n}{2}</math>.

通过从一组 n 个孤立的顶点开始，并在它们之间随机添加连续的边，得到一个随机图。这个领域的研究的目的是确定在什么阶段图形的特殊性质可能出现。不同的随机图模型在图上产生不同的概率分布。最常见的研究是由埃德加 · 吉尔伯特提出的，表示G(n,p) ，其中每个可能的边独立出现的概率为0 < ''p'' < 1。获得任意一个 m 边随机图的概率是 <math> p^m (1-p)^(n-m)</math > ，记号是 < math > n = tbinom { n }{2} </math > 。

通过从一组 n 个孤立的顶点开始，并在它们之间随机添加连续的边，得到一个随机图。这个领域的研究的目的是确定在什么阶段图形的特殊性质可能出现。不同的随机图模型在图上产生不同的概率分布。最常见的研究是由埃德加 · 吉尔伯特提出的，表示''G''(''n'',''p'') ，其中每个可能的边独立出现的概率为0 < ''p'' < 1。获得任意一个 m 边随机图的概率是 <math>p^m (1-p)^(N-m)</math > ，记号是 < math > n = tbinom { n }{2} </math >