COM 411

Today’s Dad Joke

  • Why did the near-sighted man fall in the well?
  • He couldn’t see that well

Network Data and Network Types

Homework

  • Did you find someone with a Hawaiian driver’s license?
  • How did it go?
  • What were the challenges?
  • Were there overlapping networks?

Networks in R

What is R?

Why are we using R?

  • Powerful
  • Reproducible
  • Extensible

Network Data

  • What are the three main ways of representing networks?

Matrices

## 16 x 16 sparse Matrix of class "dgCMatrix"
##                                      
##  [1,] . . . 1 1 . . . 1 . . . . . . .
##  [2,] . . 1 . . 1 . . . . . . . 1 . .
##  [3,] . 1 . . . 1 . 1 . . . . . . . .
##  [4,] 1 . . . . . 1 1 . . . . . . . 1
##  [5,] 1 . . . . 1 . 1 . . . 1 . . . .
##  [6,] . 1 1 . 1 . . . . 1 . . . . . .
##  [7,] . . . 1 . . . 1 . . 1 . . . . .
##  [8,] . . 1 1 1 . 1 . . . . 1 . 1 . .
##  [9,] 1 . . . . . . . . 1 . 1 . . . .
## [10,] . . . . . 1 . . 1 . 1 . . 1 . .
## [11,] . . . . . . 1 . . 1 . 1 . . 1 .
## [12,] . . . . 1 . . 1 1 . 1 . 1 . . 1
## [13,] . . . . . . . . . . . 1 . 1 1 1
## [14,] . 1 . . . . . 1 . 1 . . 1 . 1 .
## [15,] . . . . . . . . . . 1 . 1 1 . 1
## [16,] . . . 1 . . . . . . . 1 1 . 1 .

Edgelists

##       [,1] [,2]
##  [1,]    1    9
##  [2,]    1    5
##  [3,]    2    3
##  [4,]    2    6
##  [5,]    8   14
##  [6,]    3    6
##  [7,]    1    4
##  [8,]    4    8
##  [9,]    5    6
## [10,]    5   12
## [11,]    4    7
## [12,]    6   10
## [13,]    7    8
## [14,]    7   11
## [15,]    5    8
## [16,]    8   12
## [17,]    9   10
## [18,]   13   15
## [19,]   10   11
## [20,]   10   14
## [21,]   11   12
## [22,]   11   15
## [23,]    9   12
## [24,]   12   16
## [25,]   13   14
## [26,]   12   13
## [27,]   14   15
## [28,]    2   14
## [29,]   15   16
## [30,]    3    8
## [31,]   13   16
## [32,]    4   16

Graphs/plots/sociograms

Where does social network data come from?

  • Surveys
  • Observation
  • Trace data

Network Types

Ego networks

  • Typically created from surveys
  • Your family networks were ego networks

Extended ego networks

Bipartite Networks

  • 20 random reddittors and their communities

What does this data look like?

## # A tibble: 6 x 3
##   author        subreddit          posts
##   <chr>         <chr>              <dbl>
## 1 Arutyh        Tulpas                44
## 2 Sankakugeri   wwesupercard          38
## 3 NotAReelclown bestoflegaladvice     43
## 4 NotAReelclown shittykickstarters    14
## 5 hangryharry   churning              19
## 6 Arutyh        Pathfinder_RPG        58

Bipartite networks can be “projected”

  • People who comment in the same communities

Projections

Nodes and edges can have attributes

Edge attributes

  • Usually this is weight

Assignments

  • Homework 3 (extended family network)
  • Install R and RStudio (optional)