evcent(sna)
evcent()所属R语言包：sna
Find Eigenvector Centrality Scores of Network Positions
特征向量中心得分的网络位置
译者：生物统计家园网 机器人LoveR
描述----------Description----------
evcent takes one or more graphs (dat) and returns the eigenvector 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).
evcent需要一个或多个图形（dat）和返回的特征向量中心性的仓内的图形表示nodes（选择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----------
evcent(dat, g=1, nodes=NULL, gmode="digraph", diag=FALSE,
tmaxdev=FALSE, rescale=FALSE, ignore.eval=FALSE, tol=1e-10,
maxiter=1e5, use.eigen=FALSE)
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. This is currently ignored.
的图表类型的字符串，表示正在评估中。表明边缘应被解释为指示“有向图”，“图形”表明边缘是无向。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：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：ignore.eval
logical; should edge values be ignored?
逻辑;边缘值应该被忽略？
参数：tol
convergence tolerance for the eigenvector computation.
特征向量计算的收敛公差。
参数：maxiter
maximum iterations for eigenvector calculation.
特征向量计算的最大迭代次数。
参数：use.eigen
logical; should we use R's eigen routine instead of the (faster but less robust) internal method?
逻辑，我们应该用R的eigen程序，而不是（更快，但不太可靠）的内部方法？
Details
详细信息----------Details----------
Eigenvector centrality scores correspond to the values of the first eigenvector of the graph adjacency matrix; these scores may, in turn, be interpreted as arising from a reciprocal process in which the centrality of each actor is proportional to the sum of the centralities of those actors to whom he or she is connected. In general, vertices with high eigenvector centralities are those which are connected to many other vertices which are, in turn, connected to many others (and so on). (The perceptive may realize that this implies that the largest values will be obtained by individuals in large cliques (or high-density substructures). This is also intelligible from an algebraic point of view, with the first eigenvector being closely related to the best rank-1 approximation of the adjacency matrix (a relationship which is easy to see in the special case of a diagonalizable symmetric real matrix via the SLS^-1 decomposition).)
特征向量中心的分数对应的第一特征向量图的邻接矩阵的值，反过来，这些分数可以被解释为一个互惠的过程中，每一个演员的核心是成比例的总和，这些演员的中心性所产生的他或她被连接。在一般情况下，与高的特征向量中心性的顶点是那些，它们连接到许多其他顶点，反过来，连接到许多其他（等等）。 （感知可认识到，这意味着最大的值，将通过以下方式获得个人在大派系（或高密度的子结构）从代数的角度来看，这是可理解的，具有第一特征向量被密切相关的最优秩-1近似的邻接矩阵的关系（这是很容易看到的实对称矩阵的对角化的特殊情况下，通过分解SLS^-1））。
By default, a sparse-graph power method is used to obtain the principal eigenvector. This procedure scales well, but may not converge in some cases. In the event that the convergence objective set by tol is not obtained, evcent will return a warning message. Correctives in this case include increasing maxiter, or setting use.eigen to TRUE. The latter will cause evcent to use R's standard eigen method to calculate the principal eigenvector; this is far slower for sparse graphs, but is also more robust.
默认情况下，稀疏图的电源使用方法获得的主要特征向量。这个程序能很好地进行扩展，但可能不收敛在某些情况下。事件收敛目标设置tol未获得，evcent将返回一条警告消息。纠正在这种情况下，包括增加maxiter，，或设置use.eigenTRUE。后者将导致evcent使用R的标准eigen的方法来计算的主要特征向量，这是远远慢，对于稀疏图，但也更强大。
The simple eigenvector centrality is generalized by the Bonacich power centrality measure; see bonpow for more details.
简单的特征向量中心是，广义的Bonacich权力中心度量，看到bonpow更多的细节。
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。
警告----------WARNING ----------
evcent will not symmetrize your data before extracting eigenvectors; don't send this routine asymmetric matrices unless you really mean to do so.
evcent不会对称数据，然后提取特征向量;不发送这种常规的非对称矩阵，除非你真的是这样做的。
注意----------Note----------
The theoretical maximum deviation used here is not obtained with the star network, in general. For symmetric data, the maximum occurs for an empty graph with one complete dyad; the maximum deviation for asymmetric data is generated by the outstar. UCINET V seems not to adjust for this fact, which can cause some oddities in their centralization scores (and results in a discrepancy in centralizations between the two packages).
这里所用的理论最大偏差不能得到的星形网络，一般。对于对称的数据，最大的一个空的图形与一个完整对子发生;不对称数据的最大偏差由outstar生成。 UCINET V似乎并没有调整这一事实，可能会导致一些古怪集中的分数（在这两个包的功能中心之间的差异）。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Katz, L. (1953). “A New Status Index Derived from Sociometric Analysis.” Psychometrika, 18, 39-43.
参见----------See Also----------
centralization, bonpow
centralization，bonpow
实例----------Examples----------
#Generate some test data[生成一些测试数据。]
dat<-rgraph(10,mode="graph")
#Compute eigenvector centrality scores[计算特征向量中心得分]
evcent(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。
|
发表于 2012-9-30 10:51:10
