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A closely related model, the Erdős–Rényi model denoted G(n,M), assigns equal probability to all graphs with exactly M edges. With 0 ≤ M ≤ N, G(n,M) has <math>\tbinom{N}{M}</math> elements and every element occurs with probability <math>1/\tbinom{N}{M}</math>.  The latter model can be viewed as a snapshot at a particular time (M) of the random graph process <math>\tilde{G}_n</math>, which is a stochastic process that starts with n vertices and no edges, and at each step adds one new edge chosen uniformly from the set of missing edges.

A closely related model, the Erdős–Rényi model denoted G(n,M), assigns equal probability to all graphs with exactly M edges. With 0 ≤ M ≤ N, G(n,M) has <math>\tbinom{N}{M}</math> elements and every element occurs with probability <math>1/\tbinom{N}{M}</math>.  The latter model can be viewed as a snapshot at a particular time (M) of the random graph process <math>\tilde{G}_n</math>, which is a stochastic process that starts with n vertices and no edges, and at each step adds one new edge chosen uniformly from the set of missing edges.

一个密切相关的模型，Erdős-Rényi 模型表示''G'' (''n'',''M'')，给每一个正好有''M''条边的图赋予等概率。当0≤ ''M'' ≤ ''N'' 时，''G'' (''n'',''M'')具有 <math>\tbinom{N}{M}</math> 元素，且每个元素都以概率<math>1/\tbinom{N}{M}</math> 出现。后一个模型可以看作是随机图过程某个特定时间(''M'')的一个快照，这个时间(''M'')是从 n 个顶点开始没有边的一个随机过程，每个步骤均匀地从缺失的边集中选择一个新的边。

一个密切相关的模型，Erdős-Rényi 模型表示''G'' (''n'',''M'')，给每一个正好有''M''条边的图赋予等概率。当0≤ ''M'' ≤ ''N'' 时，''G'' (''n'',''M'')具有 <math>\tbinom{N}{M}</math> 元素，且每个元素都以概率<math>1/\tbinom{N}{M}</math> 出现。后一个模型可以看作是随机图过程<math>\tilde{G}_n</math>某个特定时间(''M'')的一个快照，这个时间(''M'')是从 n 个顶点开始没有边的一个随机过程，每个步骤均匀地从缺失的边集中选择一个新的边。