Articles - Social Network Analysis

Key features

Can be used to easily create an interactive sankey diagram, as well as, other network layout such as dendrogram, radial and diagnonal networks.

Key R functions and options

Key R functions:

forceNetwork(). Creates a D3 JavaScript force directed network graph

forceNetwork(Links, Nodes, Source, Target,
             Value, NodeID, Nodesize, Group)

Key Arguments:

• Links: edges list. Edge IDs should start with 0
• Nodes: Nodes list. Node IDs should start with 0
• Source, Target: the names of the column, in the edges data, containing the network source and target variables, respectively.
• Value: the name of the column, in the edge data, containing the weight values for edges. Used to indicate how wide the links are.
• NodeID: the name of the column, in the nodes data, containing the node IDs. Used for labeling the nodes.
• Nodesize: the name of the column, in the nodes data, with some value to vary the node radius’s with.
• Group: the name of the column, in the nodes data, specifying the group of each node.

Prepare nodes and edes data

1. Prepare the nodes and the edges data:

nodes_d3 <- mutate(nodes, id = id - 1)
edges_d3 <- mutate(edges, from = from - 1, to = to - 1)

2. Create the interactive network:

library(networkD3)

forceNetwork(
  Links = edges_d3, Nodes = nodes_d3,  
  Source = "from", Target = "to",      # so the network is directed.
  NodeID = "label", Group = "id", Value = "weight", 
  opacity = 1, fontSize = 16, zoom = TRUE
  )

Create sankey diagram

You can create a d3-styled sankey diagram. A Sankey diagram is a good fit for the phone call data. There are not too many nodes in the data, making it easier to visualize the flow of phone calls.

Create a sankey diagram:

sankeyNetwork(
  Links = edges_d3, Nodes = nodes_d3, 
  Source = "from", Target = "to", 
  NodeID = "label", Value = "weight", 
  fontSize = 16, unit = "Letter(s)")

Other hierarchical layouts exist in the network3D package to visualize tree-like graphs. In the example below, we start by computing hierarchical clustering using a sample of the USArrests data set:

set.seed(123)
hc <- USArrests %>% sample_n(15) %>%
  scale() %>% dist() %>%
  hclust(method = "complete")

Other network layouts

• dendroNetwork:

dendroNetwork(hc, fontSize = 15)

Other alternatives are:

• radialNetwork:

radialNetwork(
  as.radialNetwork(hc), fontSize = 15
  )

• diagonalNetwork:

diagonalNetwork(
  as.radialNetwork(hc), fontSize = 15
  )

Key features

• Creates interactive network graphs.
• Possible to customize nodes and edge as you want.
• Can be used to directly visualize interactively a network generated with the igraph package.
• Can be used to visualize recursive partitioning and regression trees generated with the rpart package.
• Possible to use images and icons for node shapes.
• Supports igraph layouts

Key R function and options

Key R function:

visNetwork( 
  nodes = NULL, edges = NULL,
  width = NULL, height = NULL, 
  main = NULL, submain = NULL, footer = NULL
  )

Key Arguments:

• nodes: nodes list information. Should contain at least the column “id”. See visNodes() for more options to control nodes. Other colums can be included in the data, such as:
  • “id” : id of the node, needed in edges information
  • “label” : label of the node
  • “group” : group of the node. Groups can be configure with visGroups().
  • “value” : size of the node
  • “title” : tooltip of the node
  • …
• edges: edges list information. Required at least columns “from” and “to”. See visEdges() for more options to control edges.
  • “from” : node id of begin of the edge
  • “to” : node id of end of the edge
  • “label” : label of the edge
  • “value” : size of the node
  • “title” : tooltip of the node
  • …

Create a classic network graphs

To have always the same network, you can use the function visLayout(randomSeed = 12):

library("visNetwork")

visNetwork(nodes, edges) %>%
  visLayout(randomSeed = 12) 

If you want to control the width of edges according to a variable, you should include the column “width” in the edge list data. You should manually calculate and scale the edge width.

In the following R code, we’ll customize the visNetwork() output by using an igraph layout and changing the edges width.

First add the column width in the edges list data frame. Set the minimum width to 1:

edges <- mutate(edges, width = 1 + weight/5)

Create the network graph with the variable edge widths and the igraph layout = “layout_with_fr”.

visNetwork(nodes, edges) %>% 
  visIgraphLayout(layout = "layout_with_fr") %>% 
  visEdges(arrows = "middle") %>%
  visLayout(randomSeed = 1234)  

Visualize classification and regression trees

As mentioned above, you can visualize classification and regression trees generated using the rpart package.

Key function: visTree() [in visNetwork version >= 2.0.0].

For example, to visualize a classification tree, type the following R code:

# Compute
library(rpart)
res <- rpart(Species~., data=iris)
# Visualize
visTree(res, main = "Iris classification Tree",
        width = "80%",  height = "400px")