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第81行： − + − + − − − 利用随机图的 < math > > langle k ^ 2 rangle = langle k rangle (langle k rangle + 1) </math > ，可以重新表示随机网络的临界点。+ − − − <math> − − 《数学》 − − \begin{align} − − \begin{align} − − 开始{ align } − − f_c^{ER}=1-\frac{1}{\langle k \rangle} − − 1-frac {1}{ langle k rangle } − − \end{align} − − \end{align} − − 结束{ align } − − </math> − − 数学 − 第131行：
第103行： − 随着随机网络的密度增加，临界阈值增加，这意味着必须删除更高的节点分数以断开巨型组件的连接。+ − −
→Random network
===Random network===
=== Random network 随机网络 ===
{{Main article|Erdős–Rényi model}}
{{Main article|Erdős–Rényi model}}
Using <math>\langle k^2 \rangle = \langle k \rangle(\langle k \rangle+1)</math> for an [[Erdős–Rényi model|Erdős–Rényi (ER) random graph]], one can re-express the critical point for a random network.
Using <math>\langle k^2 \rangle = \langle k \rangle(\langle k \rangle+1)</math> for an [[Erdős–Rényi model|Erdős–Rényi (ER) random graph]], one can re-express the critical point for a random network.<ref name="NetworkBook">ALBERT-LÁSZLÓ BARABÁSI. Network Science (2014).</ref>
Using <math>\langle k^2 \rangle = \langle k \rangle(\langle k \rangle+1)</math> for an Erdős–Rényi (ER) random graph, one can re-express the critical point for a random network.
Using <math>\langle k^2 \rangle = \langle k \rangle(\langle k \rangle+1)</math> for an Erdős–Rényi (ER) random graph, one can re-express the critical point for a random network.
使用 <math>\langle k^2 \rangle = \langle k \rangle(\langle k \rangle+1)</math> 表示Erdős-Rényi（ER）随机图，可以重新表达随机网络的临界点。
<math>
<math>
f_c^{ER}=1-\frac{1}{\langle k \rangle}
f_c^{ER}=1-\frac{1}{\langle k \rangle}
</math>
</math>
As a random network gets denser, the critical threshold increases, meaning a higher fraction of the nodes must be removed to disconnect the giant component.
As a random network gets denser, the critical threshold increases, meaning a higher fraction of the nodes must be removed to disconnect the giant component.
随着随机网络变得越来越密集，临界阈值会增加，这意味着必须删除更高比例的节点才能断开巨型组件的连接。
===Scale-free network===
===Scale-free network===