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第31行： − + − − While the ER model's simplicity has helped it find many applications, it does not accurately describe many real world networks. The ER model fails to generate local clustering and triadic closures as often as they are found in real world networks. Therefore, the Watts and Strogatz model was proposed, whereby a network is constructed as a regular ring lattice, and then nodes are rewired according to some probability β.<ref name=WS>{{cite journal − − 尽管ER模型的简单性帮助它找到了许多应用之处，但它并不能准确地描述许多真实世界的网络。ER模型无法像在现实世界网络中那样频繁地生成局部聚类和三元闭包。为此，提出了<font color="#ff8000">瓦茨-斯托加茨模型 Watts-Strogatz model</font>，将网络构造成规则的环网格，然后根据一定的概率β重新连接节点。 引用名称 − − ws { cite journal − − 第105行：
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第117行： − 尽管取得了这样的成就，ER 模型、 Watts-Storgatz 模型都未能解释在许多现实世界网络中观察到的中心节点的形成。ER模型中的度分布遵循泊松分佈，而 Watts-Strogatz 模型生成的图在度上是均匀的。许多网络是无标度的，这意味着它们的度分布遵循这种形式的幂律:+
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瓦茨-斯托加茨图
瓦茨-斯托加茨图
While the ER model's simplicity has helped it find many applications, it does not accurately describe many real world networks. The ER model fails to generate local clustering and [[triadic closure]]s as often as they are found in real world networks. Therefore, the [[Watts and Strogatz model]] was proposed, whereby a network is constructed as a regular ring lattice, and then nodes are rewired according to some probability '''β'''.<ref name=WS>{{cite journal
While the ER model's simplicity has helped it find many applications, it does not accurately describe many real world networks. The ER model fails to generate local clustering and [[triadic closure]]s as often as they are found in real world networks. Therefore, the [[Watts and Strogatz model]] was proposed, whereby a network is constructed as a regular ring lattice, and then nodes are rewired according to some probability '''β'''.<ref name=WS>{{cite journal | author1 = Watts, D.J.
| author1 = Watts, D.J.
| author1 = Watts, D.J.
| author1 = Watts, D.J.
| bibcode 1998 / natur. 393. . 440 w
| bibcode 1998 / natur. 393. . 440 w
}}</ref> This produces a locally clustered network and dramatically reduces the [[average path length]], creating networks which represent the [[Small-world networks|small world phenomenon]] observed in many real world networks.<ref name=milg>{{cite journal |author1=Travers Jeffrey |author2=Milgram Stanley | year = 1969 | title = An Experimental Study of the Small World Problem | url = | journal = Sociometry | volume = 32 | issue = 4| pages = 425–443 | doi=10.2307/2786545|jstor=2786545 }}</ref>
}}</ref>
While the ER model's simplicity has helped it find many applications, it does not accurately describe many real world networks. The ER model fails to generate local clustering and triadic closures as often as they are found in real world networks. Therefore, the Watts and Strogatz model was proposed, whereby a network is constructed as a regular ring lattice, and then nodes are rewired according to some probability β.<ref name=WS>{{cite journal
尽管ER模型的简单性帮助它找到了许多应用之处，但它并不能准确地描述许多真实世界的网络。ER模型无法像在现实世界网络中那样频繁地生成局部聚类和三元闭包。为此，提出了<font color="#ff8000">瓦茨-斯托加茨模型 Watts-Strogatz model</font>，将网络构造成规则的环网格，然后根据一定的概率β重新连接节点。
ws { cite journal
This produces a locally clustered network and dramatically reduces the [[average path length]], creating networks which represent the [[Small-world networks|small world phenomenon]] observed in many real world networks.<ref name=milg>{{cite journal |author1=Travers Jeffrey |author2=Milgram Stanley | year = 1969 | title = An Experimental Study of the Small World Problem | url = | journal = Sociometry | volume = 32 | issue = 4| pages = 425–443 | doi=10.2307/2786545|jstor=2786545 }}</ref>
}}</ref> This produces a locally clustered network and dramatically reduces the average path length, creating networks which represent the small world phenomenon observed in many real world networks.
}}</ref> This produces a locally clustered network and dramatically reduces the average path length, creating networks which represent the small world phenomenon observed in many real world networks.
Despite this achievement, both the ER and the Watts and Storgatz models fail to account for the formulation of hubs as observed in many real world networks. The degree distribution in the ER model follows a Poisson distribution, while the Watts and Strogatz model produces graphs that are homogeneous in degree. Many networks are instead scale free, meaning that their degree distribution follows a power law of the form:
Despite this achievement, both the ER and the Watts and Storgatz models fail to account for the formulation of hubs as observed in many real world networks. The degree distribution in the ER model follows a Poisson distribution, while the Watts and Strogatz model produces graphs that are homogeneous in degree. Many networks are instead scale free, meaning that their degree distribution follows a power law of the form:
尽管取得了这样的成就，ER模型和Watts-Storgatz模型都未能解释在许多现实世界网络中观察到的中心节点的形成。ER模型中的度分布遵循<font color="#ff8000">泊松分布</font>，而Watts-Strogatz模型生成的图在<font color="#ff8000">度</font>上是<font color="#ff8000">同质的</font>。但是，许多网络是无标度的，这意味着它们的度分布遵循以下形式的<font color="#ff8000">幂律分布</font>: