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第6行： − − 在'''<font color="#ff8000">图论 graph theory </font>'''和'''<font color="#ff8000">网络分析 network analysis </font>'''中，'''<font color="#ff8000">中心性 centrality </font>'''指标用于识别图中最重要的顶点。其应用包括在社会网络中识别出最有影响力的个人，在因特网或城市网络中识别出最为关键的基础设施节点，以及识别疾病的超级传播者。中心性的概念最初由'''<font color="#ff8000">社会网络分析 social network analysis</font>'''所发展，因此许多用于衡量中心性的术语都反映出了它们的社会学出身。 − 中心性不应与节点影响度相混淆，后者意在量化网络中每个节点的影响。+ − + − + + + − Centrality indices are answers to the question "What characterizes an important vertex?" The answer is given in terms of a real-valued function on the vertices of a graph, where the values produced are expected to provide a ranking which identifies the most important nodes.<ref name="Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista. 2018 E12201--E12208">{{cite journal | − − < ref name = “ Christian f. a. Negre，Uriel n. Morzan，Heidi p. Hendrickson，Rhitankar Pal，George p. Lisi，j. Patrick Loria，Ivan Rivalta，Junming Ho，Victor s. Batista。2018 E12201-- E12208” > { cite journal | − − author = Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista.| − − author = Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista.| − − 作者: Christian f. a. Negre，Uriel n. Morzan，Heidi p. Hendrickson，Rhitankar Pal，George p. Lisi，j. Patrick Loria，Ivan Rivalta，Junming Ho，Victor s. Batista。| − − title = Eigenvector centrality for characterization of protein allosteric pathways| − − title = Eigenvector centrality for characterization of protein allosteric pathways| − − 蛋白质变构角色塑造的特征向量中心性 − − journal = Proceedings of the National Academy of Sciences| − − journal = Proceedings of the National Academy of Sciences| − − 日记 = 美国国家科学院院刊 | − − volume = 115| − − volume = 115| − − 115 | − − number = 52| − − number = 52| − − 52 | − − pages = E12201–E12208| − − pages = E12201–E12208| − − Pages = E12201-E12208 | − − year = 2018| − − year = 2018| − − 2018年 − − doi = 10.1073/pnas.1810452115| − − doi = 10.1073/pnas.1810452115| − − Doi = 10.1073/pnas. 1810452115 | − − pmid = 30530700| − − pmid = 30530700| − − 30530700 | − − pmc = 6310864}}</ref> − − pmc = 6310864}}</ref> − − 6310864} </ref > −
无编辑摘要
In [[graph theory]] and [[network theory|network analysis]], indicators of '''centrality''' identify the most important [[vertex (graph theory)|vertices]] within a graph. Applications include identifying the most influential person(s) in a [[social network]], key infrastructure nodes in the [[Internet]] or [[urban network]]s, and [[super-spreader]]s of disease. Centrality concepts were first developed in [[social network analysis]], and many of the terms used to measure centrality reflect their [[sociology|sociological]] origin.<ref name="NewmanNetworks">Newman, M.E.J. 2010. ''Networks: An Introduction.'' Oxford, UK: Oxford University Press.</ref>
In [[graph theory]] and [[network theory|network analysis]], indicators of '''centrality''' identify the most important [[vertex (graph theory)|vertices]] within a graph. Applications include identifying the most influential person(s) in a [[social network]], key infrastructure nodes in the [[Internet]] or [[urban network]]s, and [[super-spreader]]s of disease. Centrality concepts were first developed in [[social network analysis]], and many of the terms used to measure centrality reflect their [[sociology|sociological]] origin.<ref name="NewmanNetworks">Newman, M.E.J. 2010. ''Networks: An Introduction.'' Oxford, UK: Oxford University Press.</ref>
In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super-spreaders of disease. Centrality concepts were first developed in , and many of the terms used to measure centrality reflect their sociological origin.
In graph theory and network analysis, indicators of centrality identify the most important vertices within a graph. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, and super-spreaders of disease. Centrality concepts were first developed in , and many of the terms used to measure centrality reflect their sociological origin.
They should not be confused with [[node influence metric]]s, which seek to quantify the influence of every node in the network.
They should not be confused with [[node influence metric]]s, which seek to quantify the influence of every node in the network.
They should not be confused with node influence metrics, which seek to quantify the influence of every node in the network.
They should not be confused with node influence metrics, which seek to quantify the influence of every node in the network.
在'''<font color="#ff8000">图论 graph theory </font>'''和'''<font color="#ff8000">网络分析 network analysis </font>'''中，'''<font color="#ff8000">中心性 centrality </font>'''指标用于识别图中最重要的顶点。其应用包括在社会网络中识别出最有影响力的个人，在因特网或城市网络中识别出最为关键的基础设施节点，以及识别疾病的超级传播者。中心性的概念最初由'''<font color="#ff8000">社会网络分析 social network analysis</font>'''所发展，因此许多用于衡量中心性的术语都反映出了它们的社会学出身。中心性不应与节点影响度相混淆，后者意在量化网络中每个节点的影响。
==中心性指标的定义与特性Definition and characterization of centrality indices==
==中心性指数的定义与特性Definition and characterization of centrality indices==
Centrality indices are answers to the question "What characterizes an important vertex?" The answer is given in terms of a real-valued function on the vertices of a graph, where the values produced are expected to provide a ranking which identifies the most important nodes.<ref name="Bonacich1987">{{cite journal |last1= Bonacich |first1= Phillip|year= 1987 |title= Power and Centrality: A Family of Measures | journal=American Journal of Sociology |volume= 92|issue= 5|pages= 1170–1182|doi=10.1086/228631 |url= }}<!--|accessdate=July 11, 2014--></ref><ref name="Borgatti2005">{{cite journal |last1= Borgatti |first1= Stephen P.|year= 2005 |title= Centrality and Network Flow |journal=Social Networks |volume= 27|issue= |pages= 55–71|doi=10.1016/j.socnet.2004.11.008 |url= |citeseerx= 10.1.1.387.419}}<!--|accessdate= July 11, 2014--></ref><ref name="Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista. 2018 E12201--E12208">{{cite journal |
Centrality indices are answers to the question "What characterizes an important vertex?" The answer is given in terms of a real-valued function on the vertices of a graph, where the values produced are expected to provide a ranking which identifies the most important nodes.<ref name="Bonacich1987">{{cite journal |last1= Bonacich |first1= Phillip|year= 1987 |title= Power and Centrality: A Family of Measures | journal=American Journal of Sociology |volume= 92|issue= 5|pages= 1170–1182|doi=10.1086/228631 |url= }}<!--|accessdate=July 11, 2014--></ref><ref name="Borgatti2005">{{cite journal |last1= Borgatti |first1= Stephen P.|year= 2005 |title= Centrality and Network Flow |journal=Social Networks |volume= 27|issue= |pages= 55–71|doi=10.1016/j.socnet.2004.11.008 |url= |citeseerx= 10.1.1.387.419}}<!--|accessdate= July 11, 2014--></ref><ref name="Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista. 2018 E12201--E12208">{{cite journal |author = Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista.|title = Eigenvector centrality for characterization of protein allosteric pathways|journal = Proceedings of the National Academy of Sciences|volume = 115|number = 52|pages = E12201–E12208|year = 2018|doi = 10.1073/pnas.1810452115|pmid = 30530700|pmc = 6310864}}</ref>
Centrality indices are answers to the question "What characterizes an important vertex?" The answer is given in terms of a real-valued function on the vertices of a graph, where the values produced are expected to provide a ranking which identifies the most important nodes.<ref name="Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista. 2018 E12201--E12208">{{cite journal |author = Christian F. A. Negre, Uriel N. Morzan, Heidi P. Hendrickson, Rhitankar Pal, George P. Lisi, J. Patrick Loria, Ivan Rivalta, Junming Ho, Victor S. Batista.|title = Eigenvector centrality for characterization of protein allosteric pathways|journal = Proceedings of the National Academy of Sciences|volume = 115|number = 52|pages = E12201–E12208|year = 2018|doi = 10.1073/pnas.1810452115|pmid = 30530700|pmc = 6310864}}</ref>
'''<font color="#ff8000">中心性指数Centrality indices </font>'''是对“重要顶点的特征是什么”这一问题的回答，答案以图顶点的实值函数的方式给出，在这些顶点上的取值的排序标识出最重要的节点。
'''<font color="#ff8000">中心性指数Centrality indices </font>'''是对“重要顶点的特征是什么”这一问题的回答，答案以图顶点的实值函数的方式给出，在这些顶点上的取值的排序标识出最重要的节点。