highlyConnSG(RBGL)
highlyConnSG()所属R语言包：RBGL
Compute highly connected subgraphs for an undirected graph
计算一个无向图的连通子图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute highly connected subgraphs for an undirected graph
计算一个无向图的连通子图
用法----------Usage----------
highlyConnSG(g, sat=3, ldv=c(3,2,1))
参数----------Arguments----------
参数：g
an instance of the graph class with edgemode “undirected”
graph与edgemode“无向”类的一个实例
参数：sat
singleton adoption threshold, positive integer
单身收养的阈值，正整数
参数：ldv
heuristics to remove lower degree vertice, a decreasing sequence of positive integer
启发式删除顶点程度较低，减少一个正整数序列
Details
详情----------Details----------
A graph G with n vertices is highly connected if its connectivity k(G) > n/2. The HCS algorithm partitions a given graph into a set of highly connected subgraphs, by using minimum-cut algorithm recursively. To improve performance, the approach is refined by adopting singletons, removing low degree vertices and merging clusters.
高度连接一个具有n个顶点的图G，如果其连接K（G）> N / 2。的HCS算法分区成一套高度连通子图图，利用最小切割算法递归。为了提高性能，采用单身提炼方法，消除低度顶点和合并聚类。
On singleton adoption: after each round of partition, some highly connected subgraphs could be singletons (i.e., a subgraph contains only one node). To reduce the number of singletons, therefore reduce number of clusters, we try to get "normal" subgraphs to "adopt" them. If a singleton, s, has n neighbours in a highly connected subgraph c, and n > sat, we add s to c. To adapt to the modified subgraphs, this adoption process is repeated until no further such adoption.
单身收养：每一轮分区后，一些高度连接的子图可能是单身（即一个子图只包含一个节点）。为了减少单身的数量，因此减少簇的数目，我们试图获得“正常”的子图“采取”。如果有n个单身，在一个高度连接的子Ç邻居，且n>坐，我们加s到c。以适应修改后的子图，采用过程反复进行，直到没有进一步通过。
On lower degree vertices: when the graph has low degree vertices, minimum-cut algorithm will just repeatedly separate these vertices from the rest. To reduce such expensive and non-informative computation, we "remove" these low degree vertices first before applying minimum-cut algorithm. Given ldv = (d1, d2, ..., dp), (d[i] > d[i+1] > 0), we repeat the following (i from 1 to p): remove all the highly-connected-subgraph found so far; remove vertices with degrees < di; find highly-connected-subgraphs; perform singleton adoptions.
低度顶点：图有低度顶点时，最小截算法，只是反复分开其余这些顶点。为了减少这种昂贵的和非信息的计算，我们“删除”这些低度顶点先采用最小割算法。由于LDV =（D1，D2，...，DP），（D [I]> D [I +1]> 0），我们重申以下（我从1到p）：删除所有的高连接子发现迄今删除顶点度<二，找到高度连接的子图;执行单身收养。
The Boost implementation does not support self-loops, therefore we signal an error and suggest that users remove self-loops using the function removeSelfLoops function. This change does affect degree, but the original article makes no specific reference to self-loops.
Boost的实现不支持自循环，因此，我们发出错误信号，并建议用户删除自我循环使用的功能removeSelfLoops功能。这种变化的影响程度，但原来的文章没有具体提到自我循环。
值----------Value----------
A list of clusters, each is given as vertices in the graph.
聚类名单，每个图中的顶点。
作者（S）----------Author(s)----------
Li Long <li.long@isb-sib.ch>
参考文献----------References----------
<h3>See Also</h3>
举例----------Examples----------
con <- file(system.file("XML/hcs.gxl",package="RBGL"))
coex <- fromGXL(con)
close(con)
highlyConnSG(coex)
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。
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发表于 2012-2-26 12:00:58
