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==Properties==
==Properties==
性质<br>
The theory of random graphs studies typical properties of random graphs, those that hold with high probability for graphs drawn from a particular distribution. For example, we might ask for a given value of <math>n</math> and <math>p</math> what the probability is that <math>G(n,p)</math> is [[Connection (mathematics)|connected]]. In studying such questions, researchers often concentrate on the asymptotic behavior of random graphs—the values that various probabilities converge to as <math>n</math> grows very large. [[Percolation theory]] characterizes the connectedness of random graphs, especially infinitely large ones.
The theory of random graphs studies typical properties of random graphs, those that hold with high probability for graphs drawn from a particular distribution. For example, we might ask for a given value of <math>n</math> and <math>p</math> what the probability is that <math>G(n,p)</math> is [[Connection (mathematics)|connected]]. In studying such questions, researchers often concentrate on the asymptotic behavior of random graphs—the values that various probabilities converge to as <math>n</math> grows very large. [[Percolation theory]] characterizes the connectedness of random graphs, especially infinitely large ones.
The theory of random graphs studies typical properties of random graphs, those that hold with high probability for graphs drawn from a particular distribution. For example, we might ask for a given value of <math>n</math> and <math>p</math> what the probability is that <math>G(n,p)</math> is connected. In studying such questions, researchers often concentrate on the asymptotic behavior of random graphs—the values that various probabilities converge to as <math>n</math> grows very large. Percolation theory characterizes the connectedness of random graphs, especially infinitely large ones.
The theory of random graphs studies typical properties of random graphs, those that hold with high probability for graphs drawn from a particular distribution. For example, we might ask for a given value of <math>n</math> and <math>p</math> what the probability is that <math>G(n,p)</math> is connected. In studying such questions, researchers often concentrate on the asymptotic behavior of random graphs—the values that various probabilities converge to as <math>n</math> grows very large. Percolation theory characterizes the connectedness of random graphs, especially infinitely large ones.
随机图理论研究随机图的典型性质，即从特定分布中抽取的图的高概率性质。例如，我们可以要求一个给定的值 < math > n </math > 和 < math > p </math > 什么是 < math > g (n，p) </math > 连接的概率。在研究这些问题时，研究人员往往集中在随机图的渐近行为上——各种概率收敛到的值变得非常大。渗流理论刻画了随机图，特别是无穷大图的连通性。
随机图理论研究随机图的典型性质，即从特定分布中抽取的图的高概率性质。例如，我们可以要求一个给定的值 < math >n</math > 和 < math >p</math > 什么是 < math >G(n，p)</math > 连接的概率。在研究这些问题时，研究人员往往集中在随机图的渐近行为上——各种概率收敛到的值变得非常大。<font color="##f8000">渗流理论 Percolation Theory </font>刻画了随机图，特别是无穷大图的连通性。
随机图的性质在图变换下可以改变或保持不变。例如，Mashaghi a. et A.. 证明了将随机图转换为边-对偶图(或线图)的转换产生了一个几乎具有相同度分布的图的集合，但具有度相关性和明显更高的集聚系数。
随机图的性质在图变换下可以改变或保持不变。例如，Mashaghi a. et A.. 证明了将随机图转换为边-对偶图(或线图)的转换产生了一个几乎具有相同度分布的图的集合，但具有度相关性和明显更高的集聚系数。
== Coloring ==
== Coloring ==