loadcent(sna)
loadcent()所属R语言包:sna
Compute the Load Centrality Scores of Network Positions
计算负载的网络位置的掌成绩
译者:生物统计家园网 机器人LoveR
描述----------Description----------
loadcent takes one or more graphs (dat) and returns the load centralities of positions (selected by nodes) within the graphs indicated by g. Depending on the specified mode, load on directed or undirected geodesics will be returned; this function is compatible with centralization, and will return the theoretical maximum absolute deviation (from maximum) conditional on size (which is used by centralization to normalize the observed centralization score).
loadcent需要一个或多个图形(dat)和返回的负荷中心性的仓内的图形表示nodes(选择g)。根据指定的模式将被退回,负载向或无向的测地线,这个功能是兼容centralization,将返回的理论最大绝对偏差(最大)有条件的大小(它是由<X >标准化集中观察到的得分)。
用法----------Usage----------
loadcent(dat, g = 1, nodes = NULL, gmode = "digraph", diag = FALSE,
tmaxdev = FALSE, cmode = "directed", geodist.precomp = NULL,
rescale = FALSE, ignore.eval = TRUE)
参数----------Arguments----------
参数:dat
one or more input graphs.
一个或多个输入图表。
参数:g
integer indicating the index of the graph for which centralities are to be calculated (or a vector thereof). By default, g=1.
整数,指示其中中心性计为(或它们的向量)的曲线图的索引。默认情况下,g= 1。
参数:nodes
vector indicating which nodes are to be included in the calculation. By default, all nodes are included.
矢量的指示哪些节点要被包括在计算中。默认情况下,所有的节点都包括在内。
参数:gmode
string indicating the type of graph being evaluated. digraph indicates that edges should be interpreted as directed; graph indicates that edges are undirected. gmode is set to digraph by default.
的图表类型的字符串,表示正在评估中。 digraph表示的边缘应被解释为指示;graph表明边缘是无向的。 gmode默认情况下被设置成digraph。
参数:diag
logical; should self-ties be treated as valid data. Set this true if and only if the data can contain loops. diag is FALSE by default.
逻辑;自应被视为有效数据。设置这是真的,当且仅当数据可以包含循环。 diag是FALSE默认情况下。
参数:tmaxdev
logical; return the theoretical maximum absolute deviation from the maximum nodal centrality (instead of the observed centrality scores)? By default, tmaxdev==FALSE.
逻辑;返回从最大的节点的中心性(而不是观察到的中心性分数)的理论最大绝对偏差?默认情况下,tmaxdev==FALSE。
参数:cmode
string indicating the type of load centrality being computed (directed or undirected).
字符串类型的负载核心计算(向或无向)。
参数:geodist.precomp
a geodist object precomputed for the graph to be analyzed (optional).
geodist对象的预计算的图形进行分析(可选)。
参数:rescale
logical; if true, centrality scores are rescaled such that they sum to 1.
逻辑,如果真,中心的分数重新调整,他们总结到1。
参数:ignore.eval
logical; ignore edge values when computing shortest paths?
逻辑,忽略边缘值时,计算最短路径?
Details
详细信息----------Details----------
Goh et al.'s load centrality (as reformulated by Brandes (2008)) is a betweenness-like measure defined through a hypothetical flow process. Specifically, it is assumed that each vertex sends a unit of some commodity to each other vertex to which it is connected (without edge or vertex capacity constraints), with routing based on a priority system: given an input of flow x arriving at vertex v with destination v', v divides x equally among all neigbors of minumum geodesic distance to the target. The total flow passing through a given v via this process is defined as v's load. Load is a potential alternative to betweenness for the analysis of flow structures operating well below their capacity constraints.
吴等人的负载中心性(如改写者布兰德斯(2008))是一个介假设流动过程通过一个定义的类似的措施。具体而言,它是假定每个顶点发送的某些商品的单元彼此的顶点到它所连接(没有边缘或顶点的能力的限制),路由的优先系统的基础上:给出一个输入流x到达顶点v的目的地v',v划分x平均分配给所有的minumum测地距离的目标neigbors。的总流量通过一个给定的vv的负载被定义为通过此过程。负载是一个潜在的替代品介远低于其能力的限制流结构的分析。
值----------Value----------
A vector of centrality scores.
一个向量的核心成绩。
(作者)----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Brandes, U. (2008). “On Variants of Shortest-Path Betweenness Centrality and their Generic Computation.” Social Networks, 30, 136-145.
Goh, K.-I.; Kahng, B.; and Kim, D. (2001). “Universal Behavior of Load Distribution in Scale-free Networks.” Physical Review Letters, 87(27), 1-4.
参见----------See Also----------
betweenness
betweenness
实例----------Examples----------
g<-rgraph(10) #Draw a random graph with 10 members[绘制一个随机的10名成员组成的图,]
loadcent(g) #Compute load scores[计算负荷得分]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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