infocent(sna)
infocent()所属R语言包：sna
Find Information Centrality Scores of Network Positions
查找信息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----------
infocent takes one or more graphs (dat) and returns the information centralities of positions (selected by nodes) within the graphs indicated by g. 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).
infocent需要一个或多个图形（dat）和返回的信息中心性的位置（选择nodes）内的图形表示g。此功能适用于与centralization，将返回理论上的最大绝对偏差（最大），有条件的大小（它是由centralization标准化集中观察到的得分）。
用法----------Usage----------
infocent(dat, g=1, nodes=NULL, gmode="digraph", diag=FALSE,
cmode="weak", tmaxdev=FALSE, rescale=FALSE,tol=1e-20)
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
list indicating which nodes are to be included in the calculation. By default, all nodes are included.
列出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. This is currently ignored.
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。这是目前被忽略的。
参数：diag
boolean indicating whether or not the diagonal should be treated as valid data. Set this true if and only if the data can contain loops. diag is FALSE by default.
布尔值，表示是否对角线应被视为有效的数据。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：cmode
the rule to be used by symmetrize when symmetrizing dichotomous data; must be one of "weak" (for an OR rule), "strong" for an AND rule), "upper" (for a max rule), or "lower" (for a min rule). Set to "weak" by default, this parameter obviously has no effect on symmetric data.
规则要使用的symmetrize对称二分数据时，必须是一个"weak"（的OR规则），"strong"的AND规则） "upper"（的max规则），或"lower"（的min规则）。设置为"weak"默认情况下，这个参数显然没有影响对称数据。
参数：tmaxdev
boolean indicating whether or not the theoretical maximum absolute deviation from the maximum nodal centrality should be returned. By default, tmaxdev==FALSE.
布尔值，表示是否从最大的节点的中心性的理论最大绝对偏差应返回。默认情况下，tmaxdev==FALSE。
参数：rescale
if true, centrality scores are rescaled such that they sum to 1.
如果为true，中心的分数重新调整，他们总结到1。
参数：tol
tolerance for near-singularities during matrix inversion (see solve).
容忍接近奇点矩阵求逆（见solve“）。
Details
详细信息----------Details----------
Actor information centrality is a hybrid measure which relates to both path-length indices (e.g., closeness, graph centrality) and to walk-based eigenmeasures (e.g., eigenvector centrality, Bonacich power). In particular, the information centrality of a given actor can be understood to be the harmonic average of the “bandwidth” for all paths originating with said individual (where the bandwidth is taken to be inversely related to path length). Formally, the index is constructed as follows. First, we take G to be an undirected (but possibly valued) graph – symmetrizing if necessary – with (possibly valued) adjacency matrix A. From this, we remove all isolates (whose information centralities are zero in any event) and proceed to create the weighted connection matrix
演员信息的核心是一种混合型的措施，路径长度的指数（例如，亲密，图形核心），并走eigenmeasures（例如，特征向量中心，Bonacich功率）。特别是，一个给定的演员的信息中心作用可以被理解为是所有路径始发与所述的“带宽”的谐波平均个别（其中带宽被视为于与路径长度成反比）。正式，索引被构造如下。首先，我们G是一个无方向的（但可能值）图 - 如有必要，对称 - （可能值）邻接矩阵A。在此，我们会删除所有分离株（信息中心性在任何情况下都为零），并着手建立连接矩阵的加权
C = B^-1</i>
C = B ^ -1 </ I>
where B is a pseudo-adjacency matrix formed by replacing the diagonal of 1-A with one plus each actor's degree. Given the above, let T be the trace of C with sum S_T, and let S_R be an arbitrary row sum (all rows of C have the same sum). The information centrality scores are then equal to
其中B是一个伪形成邻接矩阵的对角线取代1-A每个演员的程度。鉴于上述情况，我们T是一丝C总和S_T，让我们S_R是一个任意行总和（所有的行C有同样的总和）。信息中心地位的分数就等于
</i>
</ P>
(recalling that the scores for any omitted vertices are 0).
（回顾对任何省去的顶点的得分是0）。
In general, actors with higher information centrality are predicted to have greater control over the flow of information within a network; highly information-central individuals tend to have a large number of short paths to many others within the social structure. Because the raw centrality values can be difficult to interpret directly, rescaled values are sometimes preferred (see the rescale option). Though the use of path weights suggest information centrality as a possible replacement for closeness, the problem of inverting the B matrix poses problems of its own; as with all such measures, caution is advised on disconnected or degenerate structures.
在一般情况下，较高的信息中心地位的行动者预计将有更大的控制权在一个网络中的信息流，与高度信息中心的个人往往有大量的短路径与其他许多在社会结构。由于原料的核心价值观是难以直接解释，重新调整的值有时优选的（见rescale选项）。虽然使用的路径的权重建议的核心，作为一种可能的替代的亲密，问题的反相B矩阵提出了自己的问题，所有这些措施，谨慎，建议断开连接或退化结构。
值----------Value----------
A vector, matrix, or list containing the centrality scores (depending on the number and size of the input graphs).
含有的中心得分（取决于上的输入图形的数量和大小）的向量，矩阵，或列表。
注意----------Note----------
The theoretical maximum deviation used here is not obtained with the star network; rather, the maximum occurs for an empty graph with one complete dyad, which is the model used here.
这里所用的理论最大偏差不能得到的星形网络，而是发生，最大为一个空的一个完整的对子，这是这里所使用的模型的曲线图。
（作者）----------Author(s)----------
David Barron <a href="mailto:david.barron@jesus.ox.ac.uk">david.barron@jesus.ox.ac.uk</a>
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
参见----------See Also----------
evcent, bonpow, closeness, graphcent, centralization
evcent，bonpow，closeness，graphcent，centralization
实例----------Examples----------
#Generate some test data[生成一些测试数据。]
dat<-rgraph(10,mode="graph")
#Compute information centrality scores[计算信息核心得分]
infocent(dat)
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 10:54:51
