R:如何有效地可视化大型图网络 [英] R: How to Efficiently Visualize a Large Graph Network

问题描述

我在R中模拟了一些图形网络数据(约10,000个观测值)，并尝试使用R中的visNetwork库对其进行可视化.但是，数据非常混乱，很难进行可视化分析(我了解现实生活中，应该使用图形查询语言来分析网络数据.

I simulated some graph network data (~10,000 observations) in R and tried to visualize it using the visNetwork library in R. However, the data is very cluttered and is very difficult to analyze visually (I understand that in real life, network data is meant to be analyzed using graph query language).

目前，我有什么办法可以改善我创建的图网络的可视化(以便我可以探索彼此堆叠在一起的一些链接和节点)?

For the time being, is there anything I can do to improve the visualization of the graph network I created (so I can explore some of the linkages and nodes that are all piled on top of each other)?

可以使用"networkD3"和"diagrammeR"之类的库更好地可视化此网络吗?

Can libraries such as 'networkD3' and 'diagrammeR' be used to better visualize this network?

我在下面附加了可复制的代码:

I have attached my reproducible code below:

library(igraph)
library(dplyr)
library(visNetwork)

#create file from which to sample from
x5 <- sample(1:10000, 10000, replace=T)
#convert to data frame
x5 = as.data.frame(x5)

#create first file (take a random sample from the created file)
a = sample_n(x5, 9000)
#create second file (take a random sample from the created file)
b = sample_n(x5, 9000)

#combine
c = cbind(a,b)
#create dataframe
c = data.frame(c)
#rename column names
colnames(c) <- c("a","b")

graph <- graph.data.frame(c, directed=F)
graph <- simplify(graph)
graph

plot(graph)

library(visNetwork)
nodes <- data.frame(id = V(graph)$name, title = V(graph)$name)
nodes <- nodes[order(nodes$id, decreasing = F),]
edges <- get.data.frame(graph, what="edges")[1:2]

visNetwork(nodes, edges) %>%   visIgraphLayout(layout = "layout_with_fr") %>%
    visOptions(highlightNearest = TRUE, nodesIdSelection = TRUE) %>% 
    visInteraction(navigationButtons = TRUE)

谢谢

推荐答案

应OP的要求，我正在应用上一个答案中使用的方法 可视化将网络划分为社区的结果以解决此问题.

At the request of the OP, I am applying the method used in a previous answer Visualizing the result of dividing the network into communities to this problem.

问题中的网络不是使用指定的随机种子创建的. 在这里，我指定了可重复性的种子.

The network in the question was not created with a specified random seed. Here, I specify the seed for reproducibility.

## reproducible version of OP's network
library(igraph)
library(dplyr)

set.seed(1234)
#create file from which to sample from
x5 <- sample(1:10000, 10000, replace=T)
#convert to data frame
x5 = as.data.frame(x5)

#create first file (take a random sample from the created file)
a = sample_n(x5, 9000)
#create second file (take a random sample from the created file)
b = sample_n(x5, 9000)

#combine
c = cbind(a,b)
#create dataframe
c = data.frame(c)
#rename column names
colnames(c) <- c("a","b")

graph <- graph.data.frame(c, directed=F)
graph <- simplify(graph)

正如OP所指出的，简单的情节是一团糟.参考的先前答案 将其分为两部分:

As noted by the OP, a simple plot is a mess. The referenced previous answer broke this into two parts:

1. 绘制所有小组件
2. 绘制巨型组件

1.小型组件 不同的组件使用不同的颜色来帮助区分它们.

1. Small components Different components get different colors to help separate them.

## Visualize the small components separately
SmallV = which(components(graph)$membership != 1)
SmallComp = induced_subgraph(graph, SmallV)
LO_SC = layout_components(SmallComp, layout=layout_with_graphopt)
plot(SmallComp, layout=LO_SC, vertex.size=9, vertex.label.cex=0.8, 
    vertex.color=rainbow(18, alpha=0.6)[components(graph)$membership[SmallV]])

可以做更多的事情，但这很简单，不是问题的实质，所以我将其保留为小组件的代表.

More could be done with this, but that is fairly easy and not the substance of the question, so I will leave this as the representation of the small components.

2.巨型组件
仅仅绘制出巨大的部分仍然很难读懂.这是两个 改善显示效果的方法.两者都依赖于对顶点进行分组. 对于此答案，我将使用cluster_louvain对节点进行分组，但是您 可以尝试其他社区检测方法. cluster_louvain产生47 社区.

2. Giant component
Simply plotting the giant component is still hard to read. Here are two approaches to improving the display. Both rely on grouping the vertices. For this answer, I will use cluster_louvain to group the nodes, but you could try other community detection methods. cluster_louvain produces 47 communities.

## Now try for the giant component
GiantV = which(components(graph)$membership == 1)
GiantComp = induced_subgraph(graph, GiantV)
GC_CL = cluster_louvain(GiantComp)
max(GC_CL$membership)
[1] 47

巨型方法1-分组的顶点
创建一个强调社区的布局

Giant method 1 - grouped vertices
Create a layout that emphasizes the communities

GC_Grouped = GiantComp
E(GC_Grouped)$weight = 1
for(i in unique(membership(GC_CL))) {
    GroupV = which(membership(GC_CL) == i)
    GC_Grouped = add_edges(GC_Grouped, combn(GroupV, 2), attr=list(weight=6))
} 

set.seed(1234)
LO = layout_with_fr(GC_Grouped)
colors <- rainbow(max(membership(GC_CL)))
par(mar=c(0,0,0,0))
plot(GC_CL, GiantComp, layout=LO,
    vertex.size = 5, 
    vertex.color=colors[membership(GC_CL)], 
    vertex.label = NA, edge.width = 1)

这提供了一些见识，但是很多的边缘使其难以阅读.

This provides some insight, but the many edges make it a bit hard to read.

巨型方法2-契约社区
将每个社区绘制为单个顶点.顶点大小 反映该社区中节点的数量.颜色代表 社区节点的程度.

Giant method 2 - contracted communities
Plot each community as a single vertex. The size of the vertex reflects the number of nodes in that community. The color represents the degree of the community node.

## Contract the communities in the giant component
CL.Comm = simplify(contract(GiantComp, membership(GC_CL)))
D = unname(degree(CL.Comm))

set.seed(1234)
par(mar=c(0,0,0,0))
plot(CL.Comm, vertex.size=sqrt(sizes(GC_CL)),
    vertex.label=1:max(membership(GC_CL)), vertex.cex = 0.8,
    vertex.color=round((D-29)/4)+1)

这更干净，但是失去了社区的任何内部结构.

This is much cleaner, but loses any internal structure of the communities.

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