Rede Temporal

1 Temporal Network Analysis with R

library(sna)
library(tsna)
library(ndtv)

PHStaticEdges <- read.csv("C:/Users/user/Desktop/R/SNA/TSNA/TNAWR_StaticEdgelist.csv")
PHVertexAttributes <- read.csv("C:/Users/user/Desktop/R/SNA/TSNA/TNAWR_VertexAttributes.csv")

2 Make and visualize our static network

thenetwork <- network(PHStaticEdges,
                      vertex.attr = PHVertexAttributes, 
                      vertex.attrnames = c("vertex.id", "name", "region"),
                      directed = FALSE,
                      bipartite = FALSE)

par(mar = c(0,0,0,0))
plot(thenetwork)

3 Import Temporal Network Data

PHDynamicNodes <- read.csv("C:/Users/user/Desktop/R/SNA/TSNA/TNAWR_DynamicNodes.csv")
PHDynamicEdges <- read.csv("C:/Users/user/Desktop/R/SNA/TSNA/TNAWR_DynamicEdges.csv")

# Make the temporal network
dynamicCollabs <- networkDynamic(
  thenetwork,
  edge.spells = PHDynamicEdges,
  vertex.spells = PHDynamicNodes
)

## Edge activity in base.net was ignored
## Created net.obs.period to describe network
##  Network observation period info:
##   Number of observation spells: 1 
##   Maximal time range observed: 1257.5 until 1325 
##   Temporal mode: continuous 
##   Time unit: unknown 
##   Suggested time increment: NA

# Check the temporal network
network.dynamic.check(dynamicCollabs)

## $vertex.checks
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE
## 
## $edge.checks
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE
## 
## $dyad.checks
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE
## 
## $vertex.tea.checks
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE
## 
## $edge.tea.checks
##   [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
##  [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE
## 
## $network.tea.checks
## [1] TRUE
## 
## $net.obs.period.check
## [1] TRUE

4 Plot network dynamic object as a static image

plot(dynamicCollabs)

# Plot our dynamic network as a filmstrip
filmstrip(dynamicCollabs, displaylabels = F,
          frames = 9)

5 # Getting Animated

# Calculate how to plot an animated version of the dynamic network
compute.animation(
  dynamicCollabs,
  animation.mode = "kamadakawai",
  slice.par = list(
    start = 1260,
    end = 1300,
    interval = 1,
    aggregate.dur = 20,
    rule = "any"
  )
)

# Render the animation and open it in a web brower
render.d3movie(
  dynamicCollabs,
  displaylabels = FALSE,
  output.mode='htmlWidget',
  # This slice function makes the labels work
  vertex.tooltip = function(slice) {
    paste(
      "<b>Name:</b>", (slice %v% "name"),
      "<br>",
      "<b>Region:</b>", (slice %v% "region"))})

6 Plot formation of edges over time

plot(tEdgeFormation(dynamicCollabs, time.interval = .25))

# Changing Centrality

dynamicBetweenness <- tSnaStats(
  dynamicCollabs,
  snafun = "centralization",
  start = 1260,
  end = 1320,
  time.interval = 1,
  aggregate.dur = 20,
  FUN = "betweenness"
)
plot(dynamicBetweenness)

7 Thinking Temporally: Forward Reachable Sets

# Calculate and store the sizes of
# forward and backward reachable sets for each node
fwd_reach <- tReach(dynamicCollabs)
bkwd_reach <- tReach(dynamicCollabs, direction = "bkwd")
plot(fwd_reach, bkwd_reach)

# Calculate and plot the forward reachable paths
# of node number 3 (the Hospitaller Master)
HospitallerFwdPath <- tPath(
  dynamicCollabs,
  v = 3,
  direction = "fwd"
)
plotPaths(
  dynamicCollabs,
  HospitallerFwdPath,
  displaylabels = FALSE,
  vertex.col = "white"
)

# Calculate and plot the backward reachable paths
# of node number 3 (the Hospitaller Master)
HospitallerBkwdPath <- tPath(
  dynamicCollabs,
  v = 3,
  direction = "bkwd",
  type = 'latest.depart'
)
plotPaths(
  dynamicCollabs,
  HospitallerBkwdPath,
  path.col = rgb(0, 97, 255, max=255, alpha=166),
  displaylabels = FALSE,
  edge.label.col = rgb(0,0,0,0),
  vertex.col = "white"
)