Network Data and Network Types

What is a social network?

  • Nodes
    • Usually people
  • Edges / ties
    • Relationships between them
      • Friendship
      • Hatred
      • Advice
      • Time spent together
      • Diseases transmitted

Network Data

  • What are the three main ways of representing networks?

Matrices

Edgelists

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

Graphs/plots/sociograms

sw |>
ggraph() +
  geom_edge_fan() +
  geom_node_label(aes(label = name))

Where does social network data come from?

  • Surveys
  • Observation
  • Trace data

Network Types

Ego networks

  • Typically created from surveys
plot(make_ego_graph(sw, nodes = 1)[[1]])

Extended ego networks

Bipartite Networks

  • 20 random reddittors and their communities

What does this data look like?

head(r)
# A tibble: 6 × 3
  author        subreddit       posts
  <chr>         <chr>           <dbl>
1 whats_a_hokie Patriots           21
2 eratow        MensRights         17
3 eratow        TwoXChromosomes    38
4 OzzyKing459   RWBY               65
5 yakjack55     anything            1
6 seemls        DataHoarder         1

Bipartite networks can be “projected”

  • People who post in the same communities

Communities with the same users

Nodes and edges can have attributes

Edge attributes

  • Usually this is weight

What can we learn from networks?

  • Metrics about the network
    • Density, clustering, etc.
  • Metrics about the nodes
    • Power, influence, popularity
  • Look at how things change over time

Tuesday Lecture

Today’s Dad Joke

Shout out to people wondering what the opposite of ‘in’ is.

Housekeeping

  • Discussion questions
    • Monday at noon

Schedule

  • Housekeeping / Announcements (5-10)
  • Discussion and review (35-45)
  • Consolidation and Confusion (10-15)
  • Supplementary lectures
    • Response to needs
    • Discuss at end of class

Review

  • How did installing R + RStudio go?
  • Homework review
    • Encouragement to be prepared
    • Time to identify confusion - it’s OK to be confused!
  • Discussion questions review

Consolidation

  • What were some of the key ideas?
  • What are you thinking differently about now?
  • What are remaining questions/confusions?