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第1行：
第1行： − 第10行：
第9行： − − 图1：大脑连接性的网络表示，其中枢纽节点被突出显示]]+ − − − 图2：2005年1月15日的因特网局部图，枢纽节点被突出显示]]+ 第24行：
第20行： − 枢纽是拥有大度节点网络的一个组成部分。与网络中的其他节点相比，枢纽的链接数要大得多。无标度网络中枢纽的链接数（度）远远高于随机网络中链接数最大的节点，在保持网络的大小''N''和平均度 ''<k>''不变的情况下。枢纽的存在是随机网络和无标度网络的最大区别。在随机网络中，每个节点的度''k''是可比的，因此不可能出现枢纽节点。在无标度网络中，少数节点(即枢纽节点)具有高度值 ''k''，而其他节点只有少量的链接。+ − − − + − 随机网络（a）和无标度网络（b），在无标度网络中，大型枢纽被突出显示]]+ − − 第40行：
第32行： − 枢纽节点的出现可以用无标度网络和随机网络的区别来解释。'''<font color="#ff8000">无标度网络 Scale-Free Networks Barabási-Albert model</font>'''与'''<font color="#ff8000">随机网络 Random Networks Erdős–Rényi model</font>'''在两个方面有所不同: (a)'''<font color="#ff8000">增长 Growth</font>'''，(b)'''<font color="#ff8000">优先连接 Preferential Attachment</font>'''。{ Cite journal+
无编辑摘要
本词条由Ryan初步翻译，已由Alienxj审校。
本词条由Ryan初步翻译，已由Alienxj审校。
{{redirect|Network hub|the Ethernet technology|Ethernet hub}}
{{redirect|Network hub|the Ethernet technology|Ethernet hub}}
[[File:Network representation of brain connectivity.JPG|thumb|right|
[[File:Network representation of brain connectivity.JPG|thumb|right|
图1：Network representation of brain connectivity. Hubs are highlighted
图1：Network representation of brain connectivity. Hubs are highlighted
图1：大脑连接性的网络表示，其中枢纽被突出显示]]
[[File:Internet map 4096.png|thumb|150px|right|
[[File:Internet map 4096.png|thumb|150px|right|
图2:Partial map of the Internet based on the January 15, 2005. Hubs are highlighted
图2:Partial map of the Internet based on the January 15, 2005. Hubs are highlighted
图2：2005年1月15日的因特网局部图，其中枢纽被突出显示]]
A hub is a component of a network with a high-degree [[Vertex (graph theory)|node]]. Hubs have a significantly larger number of links in comparison with other nodes in the network. The number of links ([[Degree (graph theory)|degrees]]) for a hub in a scale-free network is much higher than for the biggest node in a random network, keeping the size ''N'' of the network and average degree ''<k>'' constant. The existence of hubs is the biggest difference between random networks and scale-free networks. In random networks, the degree ''k'' is comparable for every node; it is therefore not possible for hubs to emerge. In scale-free networks, a few nodes (hubs) have a high degree ''k'' while the other nodes have a small number of links.
A hub is a component of a network with a high-degree [[Vertex (graph theory)|node]]. Hubs have a significantly larger number of links in comparison with other nodes in the network. The number of links ([[Degree (graph theory)|degrees]]) for a hub in a scale-free network is much higher than for the biggest node in a random network, keeping the size ''N'' of the network and average degree ''<k>'' constant. The existence of hubs is the biggest difference between random networks and scale-free networks. In random networks, the degree ''k'' is comparable for every node; it is therefore not possible for hubs to emerge. In scale-free networks, a few nodes (hubs) have a high degree ''k'' while the other nodes have a small number of links.
A hub is a component of a network with a high-degree node. Hubs have a significantly larger number of links in comparison with other nodes in the network. The number of links (degrees) for a hub in a scale-free network is much higher than for the biggest node in a random network, keeping the size N of the network and average degree <k> constant. The existence of hubs is the biggest difference between random networks and scale-free networks. In random networks, the degree k is comparable for every node; it is therefore not possible for hubs to emerge. In scale-free networks, a few nodes (hubs) have a high degree k while the other nodes have a small number of links.
A hub is a component of a network with a high-degree node. Hubs have a significantly larger number of links in comparison with other nodes in the network. The number of links (degrees) for a hub in a scale-free network is much higher than for the biggest node in a random network, keeping the size N of the network and average degree <k> constant. The existence of hubs is the biggest difference between random networks and scale-free networks. In random networks, the degree k is comparable for every node; it is therefore not possible for hubs to emerge. In scale-free networks, a few nodes (hubs) have a high degree k while the other nodes have a small number of links.
枢纽是拥有大度节点网络的重要构件。与网络中的其他节点相比，枢纽拥有的链接数量明显更多。在保持网络规模''N''和平均度 ''<k>''不变的情况下，无标度网络中枢纽拥有的链接数（度）远远高于随机网络中链接数最大的节点。枢纽的存在是随机网络和无标度网络的最大区别。在随机网络中，对于每个节点而言，度''k''是相当的，因此不可能出现枢纽节点。而在无标度网络中，少数节点(即枢纽)具有较高的度值 ''k''，而其他节点则只拥有少量的链接。
== Emergence 涌现==
== Emergence 出现==
[[Image:Scale-free network sample.png|thumb|Example of a random network and a scale-free network|400px|right|
[[Image:Scale-free network sample.png|thumb|Example of a random network and a scale-free network|400px|right|
图3：Random network (a) and scale-free network (b). In the scale-free network, the larger hubs are highlighted.
图3：Random network (a) and scale-free network (b). In the scale-free network, the larger hubs are highlighted.
图3：随机网络（a），无标度网络（b）。在无标度网络中，大型枢纽被突出显示。]]
Emergence of hubs can be explained by the difference between scale-free networks and random networks. Scale-free networks ([[Barabási–Albert model]]) are different from random networks ([[Erdős–Rényi model]]) in two aspects: (a) growth, (b) preferential attachment.<ref name=RMP>{{Cite journal
Emergence of hubs can be explained by the difference between scale-free networks and random networks. Scale-free networks ([[Barabási–Albert model]]) are different from random networks ([[Erdős–Rényi model]]) in two aspects: (a) growth, (b) preferential attachment.<ref name=RMP>{{Cite journal
Emergence of hubs can be explained by the difference between scale-free networks and random networks. Scale-free networks (Barabási–Albert model) are different from random networks (Erdős–Rényi model) in two aspects: (a) growth, (b) preferential attachment.<ref name=RMP>{{Cite journal
Emergence of hubs can be explained by the difference between scale-free networks and random networks. Scale-free networks (Barabási–Albert model) are different from random networks (Erdős–Rényi model) in two aspects: (a) growth, (b) preferential attachment.<ref name=RMP>{{Cite journal
枢纽的出现可以用无标度网络和随机网络的区别来解释。'''<font color="#ff8000">无标度网络 Scale-Free Networks</font>'''（Barabási-Albert模型）与'''<font color="#ff8000">随机网络 Random Networks</font>'''（Erdős–Rényi model）的不同主要存在于如下两个方面: （a）'''<font color="#ff8000">增长 Growth</font>'''，（b）'''<font color="#ff8000">优先连接 Preferential Attachment</font>'''。{ Cite journal
| url = http://www.nd.edu/~networks/Publication%20Categories/03%20Journal%20Articles/Physics/StatisticalMechanics_Rev%20of%20Modern%20Physics%2074,%2047%20(2002).pdf
| url = http://www.nd.edu/~networks/Publication%20Categories/03%20Journal%20Articles/Physics/StatisticalMechanics_Rev%20of%20Modern%20Physics%2074,%2047%20(2002).pdf