Network Visualization with R

The theme for the third week of Research Data and Digital Scholarship Data Jam 2021 was “Visualizing Text and Networks”.  

The “Network Visualization with R” workshop was a live-coding session which detailed the following: 

  1. Glossary of Network Visualization Terms 

  1. Software and Programming Alternatives to Network Visualization with igraph and NetworkD3 

  1. Resources 

The R and RStudio demonstration included: 

  • Nodes & Edges 

  • Directed & Undirected Graph 

  • Static and Dynamic Graphs 

  • Customizations with layout, colors, & size 

Here is the link to the Video Recording of the Workshop.

Here is the link to the workshop materials including the R exercise code, completed exercise PDF, and Philadelphia Historical Streets Dataset used in the exercise.

Network visualization allows the viewer to understand a lot of information quickly and intuitively about the state of the network at a glance. Social networks have actors with relations between them. Network diagrams (or Graphs) show interconnections between a set of entities. Each entity is represented by a Node (or Vertice). Connections between nodes are represented by links (or edges). 

original graph with six nodes and outer connected edges, modified graph with an edge added, and a modified graph with a node removed

R Packages commonly used for network visualization include: 

Other relevant R Packages include: 

  • Static Network Visualization: 

    • igraph 

    • ggraph 

    • network 

    • sna 

    • threejs 

    • ndtv 

    • statnet 

    • ggiraph 

  • Dynamic Network Visualization: 

    • networkD3 

    • VizNetwork 

The Python library alternatives are: 

  • NetworkX 

  • igraph 

The Julia library alternatives: 

  • JuliaGraph 

Software alternatives for network visualization are: 

  • Gephi

  • Cytoscape 

  • Palladio 

  • Pajek 

For the RStudio demonstration, after installing and running igraph package, a variety of graphs were displayed.  

# Install igraph library 
install.packages("igraph") 
# Run igraph library 
library(igraph) 

These included graphs with different number of nodes and edges, directed and undirected graphs, and graphs of different shapes. For example:

# Create a directed graph with 3 nodes 1, 2, and 3 
g3d <- graph( edges=c(1,2, 2,3, 3, 1), n=3, directed=T)  
plot(g3d) 
undirected graph with 3 nodes 1 2 and 3

A simple graph was developed in the form of visualizing a network of historical roads in Philadelphia where the starting point of a street and the ending point of the street are the source and destination, respectively.  

## Interactive Network Graphs 
## Historic Streets of Philadelphia 
         library(networkD3) 
# Load data 
          edges <- read.csv("Historic_Streets.csv") 
          head(edges) 
          # Identify Source and Target Edges 
           source <- edges["FROM_STREET"] 
                     target <- edges["TO_STREET"] 
# Format into network data 
                     networkData <-data.frame(source, target)   
           simpleNetwork(networkData, nodeColour= "green", zoom=T,  
                                                       height = 300, width = 300, fontSize = 16) 
Historic Streets of Philly network of streets around UPenn

The network of interactions in the book Les Misérables by Victor Hugo was also mapped as a force-directed network graph. 

## Network of Interactions in Les Miserables characters 
# Load data 
         data(MisLinks) 
         data(MisNodes) 
  
head(MisNodes) 
         head(MisLinks) 
  
# Plot 
forceNetwork(Links = MisLinks, Nodes = MisNodes, 
                                       Source = "source", Target = "target", 
                              Value = "value", opacityNoHover = 0.9, NodeID = "name", 
                                        Group = "group", opacity = 0.8, zoom=T) 
# Find all links to Valjean 
         which(MisNodes == "Valjean", arr = TRUE)[1] - 1 

 

Les Miserables Character Interactions Network graph

This graph shows the network of character interactions occurring across the novel at a glance.

Finally, a study of Zachary’s Karate Club Network helped learners understand the use-case of Network Analysis beyond network visualization. 

## Zachary's Karate Club Network  
library(igraphdata) 
          data(karate) 
head(karate) 
# How many factions are in the karate club? 
plot(karate) 
# How many subcommunities clusters are in the karate club? 
cfg <- cluster_fast_greedy(karate) 
          plot(cfg, karate) 
3 different Karate Club communities visualized as networks

This graph represents the three major communities and sub-communities within in the network.

It is important to keep in mind the accessibility concerns of network visualizations. For example, overlapping nodes might make the graph seem populated with lower number of nodes. While changing the size and color of nodes might be helpful to discern or distinguish the types of nodes, it might lead to confusion. The 2D graph might be mistaken for occupying 3D space. It is also helpful to use the ColorBrewer2 app to confirm the accessibility features of the colors used in the graph.

Other ways to distinguish nodes, edges, clusters, and networks are as following:

# nodes/vertices: color, shape, size, border, labels ,text-size, position 
# edges/links: color, size, labels, arrows, curvature, link distance 
# groups/communities: color, shading, border, lines 
# graph: layout, color, faceting 

Resources: 

Terminology 

Author
jajk@upenn.edu
Label
R
network analysis
Home page Slide titled "Data Jam 2021 Network Visualization with R" watermarked "Penn Libraries" logo and "Research Data and Digital Scholarship" Data Jam logo of a cat in a jam jar with Copyright Commons CC-BY 4.0 linked on the page
Featured Image Caption
Data Jam 2021 "Network Visualization with R" Title Slide