网络分析

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网络分析

文章原来地址来源：sna.stanford.edu/lab.php?l=1 入门实验室 ＃这个实验的重点是向学生介绍两个包SNA和 Igraph，以涵盖一些基本的R命令，加载和管理数据，以生成图形可视化，并导出数据在别处使用。

所有需要安装的包的地址如下，建议直接安装 source(“sna.stanford.edu/setup.R“)

install.packages("ergm", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("reshape", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("igraph", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("sna", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("numDeriv", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("MatchIt", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("coin", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("boot", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("Hmisc", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("lattice", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("triads", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("psych", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("nFactors", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("animation", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("NetData", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("NetCluster", repos = "cranr.berkeley.edu/", dependencies = TRUE) install.packages("igraphtosonia", repos = "cranr.berkeley.edu/", dependencies = TRUE)

第一个实验需要加载包

library(igraph)

如果需要把这个包删除请执行

detach(package:igraph) 第一步 导入数据

用这个函数read.table()

advice_data_frame <- read.table('sna.stanford.edu/sna_R_labs/data/Krack-High-Tec-edgelist-Advice.txt') friendship_data_frame <- read.table('sna.stanford.edu/sna_R_labs/data/Krack-High-Tec-edgelist-Friendship.txt') reports_to_data_frame <- read.table('sna.stanford.edu/sna_R_labs/data/Krack-High-Tec-edgelist-ReportsTo.txt')

如果你的数据是个文件，放在某个地方，可以这样做

setwd('path/to/your_directory') your_data_frame <- read.table('your_file_name')

看数据表的前6行

head(friendship_data_frame)

或者后6行

tail(reports_to_data_frame)

或者以表格的形式打开

fix(reports_to_data_frame)

如何读取csv格式表格

attributes <- read.csv('sna.stanford.edu/sna_R_labs/data/Krack-High-Tec-Attributes.csv', header=T) attributes

分隔文本的文件如何打开，通过设置SEP达到目的

f <- read.delim("tab_delimited_file.txt") f <- read.delim("colon_delimited_file.txt", sep=':')

同理 read.spss() 读取spss文件 STATA files via read.dta(). 数据直接来自文件包里的时候，data(kracknets, package = “NetData”)

第二步 载入数据 将标题的名字换一个

colnames(advice_data_frame) <- c('ego', 'alter', 'advice_tie') head(advice_data_frame) colnames(friendship_data_frame) <- c('ego', 'alter', 'friendship_tie') head(friendship_data_frame) colnames(reports_to_data_frame) <- c('ego', 'alter', 'reports_to_tie') head(reports_to_data_frame) fix(advice_data_frame) fix(friendship_data_frame) fix(reports_to_data_frame)

验证每张表相同参数的字段是否一致

advice_data_frame$ego == friendship_data_frame$ego which(advice_data_frame$ego != friendship_data_frame$ego) which(advice_data_frame$alter != friendship_data_frame$alter) which(reports_to_data_frame$alter != friendship_data_frame$alter) which(reports_to_data_frame$ego != friendship_data_frame$ego)

合并到一张表

krack_full_data_frame <- cbind(advice_data_frame, friendship_data_frame$friendship_tie, reports_to_data_frame$reports_to_tie) head(krack_full_data_frame) names(krack_full_data_frame)[4:5] <- c("friendship_tie", "reports_to_tie") head(krack_full_data_frame)

另外一种方式合并

krack_full_data_frame <- data.frame(ego = advice_data_frame[,1], alter = advice_data_frame[,2], advice_tie = advice_data_frame[,3], friendship_tie = friendship_data_frame[,3], reports_to_tie = reports_to_data_frame[,3]) head(krack_full_data_frame)

把数据集中没有共同边的数据直接去掉了 (至少有一个大于0 ) 逻辑运算： &，|，!。(与，或，非。)

krack_full_nonzero_edges <- subset(krack_full_data_frame, (advice_tie > 0 | friendship_tie > 0 | reports_to_tie > 0)) head(krack_full_nonzero_edges)

导入数据到graph图表，默认前两列是图的节点，后面的参数是边的属性。

krack_full <- graph.data.frame(krack_full_nonzero_edges) summary(krack_full)

为了得到边的特性用到get.edge.attribute函数，其实还是最后一列的数据 只是单独提取出来了。

get.edge.attribute(krack_full, 'advice_tie') get.edge.attribute(krack_full, 'friendship_tie') get.edge.attribute(krack_full, 'reports_to_tie')

as.undirected()改成无向图，把不对称的图形变成对称的图形

krack_full_symmetrized <- as.undirected(krack_full, mode='collapse') summary(krack_full_symmetrized)

第三部分 把顶点的信添加到图中 iterate 将属性添加到图形对象的一种方法是迭代，通过每个属性和每个顶点。 这意味着我们会，一次添加一个属性到网络中的每个顶点。

for (i in V(krack_full)) { for (j in names(attributes)) { krack_full <- set.vertex.attribute(krack_full, j, index = i, attributes[i + 1, j]) } }

只读属性名称 attributes = cbind(1:length(attributes[,1]), attributes)krack_full <- graph.data.frame(d = krack_full_nonzero_edges, vertices = attributes) summary(krack_full) 点的信

get.vertex.attribute(krack_full, 'AGE') get.vertex.attribute(krack_full, 'TENURE') get.vertex.attribute(krack_full, 'LEVEL') get.vertex.attribute(krack_full, 'DEPT')

第四部、可视化网络图

画个简单的图形，设置出图形的存储位置。

setwd("") pdf("1.1_Krackhardt_Full.pdf") plot(krack_full) dev.off()

单个因素的无向图的 绘制 advice only

krack_advice_only <- delete.edges(krack_full, E(krack_full)[get.edge.attribute(krack_full, name = "advice_tie") == 0]) summary(krack_advice_only) pdf("1.2_Krackhardt_Advice.pdf") plot(krack_advice_only) dev.off()

同理 friendship only

krack_friendship_only <- delete.edges(krack_full, E(krack_full)[get.edge.attribute(krack_full, name = "friendship_tie") == 0]) summary(krack_friendship_only) pdf("1.3_Krackhardt_Friendship.pdf") plot(krack_friendship_only) dev.off()

reports-to only 同理

krack_reports_to_only <- delete.edges(krack_full, E(krack_full)[get.edge.attribute(krack_full, name = "reports_to_tie") == 0]) summary(krack_reports_to_only) pdf("1.4_Krackhardt_Reports.pdf") plot(krack_reports_to_only) dev.off()

*****the layout algorithm chterman-Rheingold*****重点内容**

reports_to_layout <- layout.fruchterman.reingold(krack_reports_to_only) pdf("1.5_Krackhardt_Reports_Fruchterman_Reingold.pdf") plot(krack_reports_to_only, layout=reports_to_layout) dev.off()

现在让我们按部门对顶点进行颜色编码并清理 通过去除顶点标签并缩小箭头大小来绘制阴谋。

dept_vertex_colors = get.vertex.attribute(krack_full,"DEPT") colors = c('Black', 'Red', 'Blue', 'Yellow', 'Green') dept_vertex_colors[dept_vertex_colors == 0] = colors[1] dept_vertex_colors[dept_vertex_colors == 1] = colors[2] dept_vertex_colors[dept_vertex_colors == 2] = colors[3] dept_vertex_colors[dept_vertex_colors == 3] = colors[4] dept_vertex_colors[dept_vertex_colors == 4] = colors[5] pdf("1.6_Krackhardt_Reports_Color.pdf") plot(krack_reports_to_only, layout=reports_to_layout, vertex.color=dept_vertex_colors, vertex.label=NA, edge.arrow.size=.5) dev.off()

设置顶点的大小

tenure_vertex_sizes = get.vertex.attribute(krack_full,"TENURE") pdf("1.7_Krackhardt_Reports_Vertex_Size.pdf") plot(krack_reports_to_only, layout=reports_to_layout, vertex.color=dept_vertex_colors, vertex.label=NA, edge.arrow.size=.5, vertex.size=tenure_vertex_sizes) dev.off()

advice and friendship ties in red and blue. 给其他两个属性添加颜色

tie_type_colors = c(rgb(1,0,0,.5), rgb(0,0,1,.5), rgb(0,0,0,.5)) E(krack_full)$color[ E(krack_full)$advice_tie==1 ] = tie_type_colors[1] E(krack_full)$color[ E(krack_full)$friendship_tie==1 ] = tie_type_colors[2] E(krack_full)$color[ E(krack_full)$reports_to_tie==1 ] = tie_type_colors[3] E(krack_full)$arrow.size=.5 V(krack_full)$color = dept_vertex_colors V(krack_full)$frame = dept_vertex_colors pdf("1.8_Krackhardt_Overlayed_Ties.pdf") plot(krack_full, layout=reports_to_layout, vertex.color=dept_vertex_colors, vertex.label=NA, edge.arrow.size=.5, vertex.size=tenure_vertex_sizes)

添加图例

legend(1, 1.25, legend = c('Advice', 'Friendship', 'Reports To'), col = tie_type_colors, lty=1, cex = .7) dev.off()

另外一种方式显示数据结构布局和最终的图形数据换一个参考标准

pdf("1.9_Krackhardt_Overlayed_Structure.pdf") plot(krack_friendship_only, layout=reports_to_layout, vertex.color=dept_vertex_colors, vertex.label=NA, edge.arrow.size=.5, vertex.size=tenure_vertex_sizes, main='Krackhardt High-Tech Managers') dev.off()

第五部分 导出这个网络图的数据

write.graph(krack_full, file='krack_full.dl', format="pajek") write.graph(krack_full, file='krack_full.txt', format="edgelist")

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