kcores(sna)
kcores()所属R语言包：sna
Compute the k-Core Structure of a Graph
计算结构图的k-核
译者：生物统计家园网 机器人LoveR
描述----------Description----------
kcores calculates the k-core structure of the input network, using the centrality measure indicated in cmode.
kcores计算K-输入网络的核心结构，表示在cmode使用中的核心措施。
用法----------Usage----------
kcores(dat, mode = "digraph", diag = FALSE, cmode = "freeman",
ignore.eval = FALSE)
参数----------Arguments----------
参数：dat
one or more (possibly valued) graphs.
一个或多个（可能值）的图表。
参数：mode
"digraph" for directed data, otherwise "graph".
"digraph"定向数据，否则"graph"。
参数：diag
logical; should self-ties be included in the degree calculations?
逻辑，包括在自度计算？
参数：cmode
the degree centrality mode to use when constructing the cores.
degree核心模式时使用的建设的核心。
参数：ignore.eval
logical; should edge values be ignored when computing degree?
逻辑，边缘值计算时被忽略的程度呢？
Details
详细信息----------Details----------
Let G=(V,E) be a graph, and let f(v,S,G) for v in V, S subseteq V be a real-valued vertex property function (in the language of Batagelj and Zaversnik). Then some set V subseteq H is a generalized k-core for f if H is a maximal set such that f(v,H,G)>=k for all v in H. Typically, f is chosen to be a degree measure with respect to S (e.g., the number of ties to vertices in S). In this case, the resulting k-cores have the intuitive property of being maximal sets such that every set member is tied (in the appropriate manner) to at least k others within the set.
让我们G=(V,E)是一个图，让f(v,S,G)：v in V, S subseteq V是一个实值的顶点属性函数（在的语言的Batagelj和Zaversnik），。然后一些设置V subseteq H是一个广义的k-核f如果H是一组最大f(v,H,G)>=k用于所有v in H。通常情况下，f就选择了一定程度的措施S（例如，关系到顶点的数量在S）。在这种情况下，所得到的k-核具有的最大集，使得每一套构件绑（以适当的方式），以集内的至少k别人的直觉属性。
Degree-based k-cores are a simple tool for identifying well-connected structures within large graphs. Let the core number of vertex v be the value of the highest-value core containing v. Then, intuitively, vertices with high core numbers belong to relatively well-connected sets (in the sense of sets with high minimum internal degree). It is important to note that, while a given k-core need not be connected, it is composed of subsets which are themselves well-connected; thus, the k-cores can be thought of as unions of relatively cohesive subgroups. As k-cores are nested, it is also natural to think of each k-core as representing a “slice” through a hypothetical “cohesion surface” on G. (Indeed, k-cores are often visualized in exactly this manner.)
度为基础的k-核是一个简单的工具，在大型图形识别连接的结构。让核心数量的顶点v包含v的价值最高的价值核心。然后，直观，高芯数的顶点属于相对完善的连接套（在这个意义上套最小程度高）。重要的是要注意的是，当一个给定的k-核不需要连接，它是由本身连接的子集，因此，k-核可以被认为相对凝聚力分组作为工会。由于k-核是嵌套的，它也是自然的“切片”，通过“假设的”凝聚力表面G想每个k-核心。 （事实上，k-核往往以同样的方式显示。）
The kcores function produces degree-based k-cores, for various degree measures (with or without edge values). The return value is the vector of core numbers for V, based on the selected degree measure. Missing (i.e., NA) edge are removed for purposes of the degree calculation.
kcores功能度为基础的k-核，为不同程度的措施（带或不带边缘值）。返回值是V，基于所选程度措施的向量核心号码。缺少（即NA）边被删除度计算的目的。
值----------Value----------
A vector containing the maximum core membership for each vertex.
一个向量，包含为每个顶点的最大核心成员。
（作者）----------Author(s)----------
Carter T. Butts <a href="mailto:buttsc@uci.edu">buttsc@uci.edu</a>
参考文献----------References----------
Batagelj, V. and Zaversnik, M. (2002). “An <code>O(m)</code> Algorithm for Cores Decomposition of Networks.” arXiv:cs/0310049v1
Batagelj, V. and Zaversnik, M. (2002). “Generalized Cores.” arXiv:cs/0202039v1
Wasserman, S. and Faust,K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
参见----------See Also----------
degree
degree
实例----------Examples----------
#Generate a graph with core-periphery structure[生成图形核心 - 边缘结构]
cv<-runif(30)
g<-rgraph(30,tp=cv%o%cv)
#Compute the k-cores based on total degree[计算基于总度的k-核]
kc<-kcores(g)
kc
#Plot the result[绘制的结果]
gplot(g,vertex.col=kc)
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 10:55:27
