cutpoints(sna)
cutpoints()所属R语言包：sna
Identify the Cutpoints of a Graph or Digraph
确定分割点的图或有向图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
cutpoints identifies the cutpoints of an input graph. Depending on mode, either a directed or undirected notion of “cutpoint” can be used.
cutpoints标识的输入图的分割点。根据mode，要么一个定向的或无向的“分割点”的概念可以用。
用法----------Usage----------
cutpoints(dat, mode = "digraph", connected = c("strong","weak","recursive"),
return.indicator = FALSE)
参数----------Arguments----------
参数：dat
one or more input graphs.
一个或多个输入图表。
参数：mode
"digraph" for directed graphs, or "graph" for undirected graphs.
"digraph"对于有向图，或"graph"无向图。
参数：connected
string indicating the type of connectedness rule to apply (only relevant where mode=="digraph").
只有相关的字符串表示的类型的连通性规则的适用（其中mode=="digraph"）。
参数：return.indicator
logical; should the results be returned as a logical (TRUE/FALSE) vector of indicators, rather than as a vector of vertex IDs?
逻辑;结果应该返回一个逻辑（TRUE/FALSE）的矢量的指标，而不是作为一个向量的顶点的ID？
Details
详细信息----------Details----------
A cutpoint (also known as an articulation point or cut-vertex) of an undirected graph, G is a vertex whose removal increases the number of components of G. Several generalizations to the directed case exist. Here, we define a strong cutpoint of directed graph G to be a vertex whose removal increases the number of strongly connected components of G (see component.dist). Likewise, weak and recursive cutpoints of G are those vertices whose removal increases the number of weak or recursive cutpoints (respectively). By default, strong cutpoints are used; alternatives may be selected via the connected argument.
甲分割点（也被称为一个铰接点或切口顶点）的无向图，G是一个顶点，其去除G的组件的数目增加。一些概括指示的情况下存在。在这里，我们定义了一个强大的分割点有向图G是一个顶点，其去除强连通分量的数量增加G（见component.dist）。同样，弱和递归分割点G的顶点去除弱或的递归分割点（分别）的数量增加的。默认情况下，强烈的切点是使用的替代品可以选择通过connected参数。
Cutpoints are of particular interest when seeking to identify critical positions in flow networks, since their removal by definition alters the connectivity properties of the graph. In this context, cutpoint status can be thought of as a primitive form of centrality (with some similarities to betweenness).
有特别的兴趣，在寻求确定在流动网络的关键位置，因为他们搬迁的定义改变了连接性能的图形分割点。在此背景下，可以想到的分割点的状态作为一个原始形式的中心性（有一些相似之处betweenness）。
Cutpoint computation is significantly faster for the undirected case (and for the weak/recursive cases) than for the strong directed case. While calling cutpoints with mode="digraph" on an undirected graph will give the same answer as mode="graph", it is thus to one's advantage to use the latter form. Do not, however, employ mode="graph" with directed data, unless you enjoy unpredictable behavior.
分割点计算是显着更快的无向的情况下（和的弱/递归的情况下）比强指示的情况下。在调用cutpoints与mode="digraph"了一个无向图，给出相同的答案mode="graph"，因此，使用后一种形式的优势。 ，但是，不要采用mode="graph"定向数据的，除非你享受不可预知的行为。
值----------Value----------
A vector of cutpoints (if return.indicator==FALSE), or else a logical vector indicating cutpoint status for each vertex.
分割点的矢量（如果return.indicator==FALSE），否则逻辑矢量切割点为每个顶点的状态。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Berge, Claude. (1966). The Theory of Graphs. New York: John Wiley and Sons.
参见----------See Also----------
component.dist, bicomponent.dist, betweenness
component.dist，bicomponent.dist，betweenness
实例----------Examples----------
#Generate some sparse random graph[产生一些稀疏的随机图]
gd<-rgraph(25,tp=1.5/24) #Directed[导演]
gu<-rgraph(25,tp=1.5/24,mode="graph") #Undirected[无向]
#Calculate the cutpoints (as an indicator vector)[计算分割点（作为一个指标向量）]
cpu<-cutpoints(gu,mode="graph",return.indicator=TRUE)
cpd<-cutpoints(gd,return.indicator=TRUE)
#Plot the result[绘制的结果]
gplot(gu,gmode="graph",vertex.col=2+cpu)
gplot(gd,vertex.col=2+cpd)
#Repeat with alternate connectivity modes[重复备用连接模式]
cpdw<-cutpoints(gd,connected="weak",return.indicator=TRUE)
cpdr<-cutpoints(gd,connected="recursive",return.indicator=TRUE)
#Visualize the difference[可视化差异]
gplot(gd,vertex.col=2+cpdw)
gplot(gd,vertex.col=2+cpdr)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 10:50:02
