2026-5-4

Groups!

##           group 1         group 2         group 3        group 4
## 1 Batson, Anthony    Mendoza, Ava   Pacheco, Alex           <NA>
## 2    Myoung, Sein Randall, Javion Bell, Mary Rose    Ong, Alyssa
## 3     Qin, Celine Kang, Christine     Smith, Reid    Pham, Canon
## 4                   Leahy, Olivia  Knowles, Genny Devir, Lindsey
##             group 5
## 1 Wolfenstein, Luci
## 2 Mahoney, Brigette
## 3      Barga, Jolie
## 4     Moore, Allana

Warm-up

  • Create a visualization with the provided network data
##           group 1         group 2         group 3        group 4
## 1 Batson, Anthony    Mendoza, Ava   Pacheco, Alex           <NA>
## 2    Myoung, Sein Randall, Javion Bell, Mary Rose    Ong, Alyssa
## 3     Qin, Celine Kang, Christine     Smith, Reid    Pham, Canon
## 4                   Leahy, Olivia  Knowles, Genny Devir, Lindsey
##             group 5
## 1 Wolfenstein, Luci
## 2 Mahoney, Brigette
## 3      Barga, Jolie
## 4     Moore, Allana

Warm-up

  • Create a visualization with the provided network data
  • How did you represent the data?
  • What are the important components?
  • What message do you seek to communicate?
  • What challenges/questions came up?

Plotting Networks

  • On Wednesday, we will plot networks!

Today’s Class

  • Warm-up: network data
  • What is network data?
  • Network metrics
  • Activity: Calculating networks by hand
  • Mid-quarter evaluation

Wednesday’s Class

  • Introduction to igraph
  • Network centrality with authorship data
  • Network data and tidycensus

Office Hours

  • Office Hours:Friday 1:30-3:30pm (Tyler)
  • Tuesdays, 10:30am-12:00pm (Yao)

Miscellaneous

  • Final Project: Collaboration encouraged, track individual contributions (e.g. through GitHub)
  • Final Project Rubric: Will be available this week
  • May 13th: Guest speaker from Recidiviz

Event of Interest

Learning Goals

  • Motivate examination of network data
  • Understand network structure (node vs. edge, directed vs. undirected)
  • Understand different types of centrality and what these mean
  • (Wednesday) Learn how to plot networks using igraph

What is Network Data?

Why Networks?

  • Up to this point, our units of analysis have mostly been people and places
  • What if we want to study connections between people or places?

Why Networks?

  • Activity Spaces
  • Friendship ties
  • Funding connections
  • Migration flows

Basics of Network Data

  • Two elements:
  1. Nodes
  2. Edges
Network example. From Nykamp DQ, An introduction to networks.

Network example. From Nykamp DQ, An introduction to networks.

What Are Networks?

  • Two types of edges:
  1. Directed
  • e.g. cash payments, job applications, academic citations, etc.
  1. Undirected
Directed Network. From [Nykamp DQ, An introduction to networks.

Directed Network. From [Nykamp DQ, An introduction to networks.

What Are Networks?

  • Two types of edges:
  1. Directed
  2. Undirected
  • e.g. friendship ties, common board members, shared destinations, etc.
Undirected Network. From Nykamp DQ, An introduction to networks.

Undirected Network. From Nykamp DQ, An introduction to networks.

Friendship Networks?

From McMillan, 2019: Friendship network at Sunshine High School by immigrant generation status. Circles represent students, and curved lines represent friendships. For the purpose of this illustration, both reciprocated and nonreciprocated friendships have been graphed.

From McMillan, 2019: Friendship network at Sunshine High School by immigrant generation status. Circles represent students, and curved lines represent friendships. For the purpose of this illustration, both reciprocated and nonreciprocated friendships have been graphed.

Funding Flows

What Are Networks?

  • In groups:
  • In the warm up, what are the nodes? edges?
  • Is the data undirected or directed?
  • We can modify the nodes and edges according to various characteristics to make our visuals more interesting

Data Visualization

  • Nodes are users
  • Edges are twitter interactions
  • Colors based on opinions

Data Visualization

  • Nodes are actors
  • Edges are information shared
  • Color based on agreement with ‘There should be an international binding commitment on all nations to reduce GHG emissions’.

How to Represent Network Data

  • One-mode vs. Two-mode

Network Visualization

  • Some networks have multiple “modes” or levels
  • For example, two-mode network below
  • Small nodes are actors, large nodes are organizations:

Network Visualization

  • Can represent this as one-mode network
  • Now ties represent shared actors
  • Colors represent funding from Koch/Exxon (green) or not (red)

Network Visualization

  • In pairs:
  • How would you describe the following network? (one mode, two mode)
  • Could it be projected to a one-mode network? How?

Recap of Network Basics

  • Nodes and edges are the building blocks of networks
  • Networks can be directed or undirected
  • Two mode networks have two levels (e.g. individuals, institutions)

Measures of Centrality

Why Centrality?

  • We might want to evaluate the central nodes in our network
  • For example: centrality in a network might represent social capital or influence

Measures of Centrality

  • Degree Centrality
  • Node with the highest number of ties is most central
  • Think: most friends

Measures of Centrality

  • Betweenness Centrality
  • Based on “shortest paths” between nodes, node involved in most “shortest paths” is most central
  • Think: who is involved in the most friendship pathways?

Measures of Centrality

  • Closeness Centrality
  • Based on “shortest paths” between nodes, node with lowest average “shortest path” to the others is most central
  • Think: who could spread a message fastest?

Measures of Centrality

  • Eigenvalue Centrality
  • Based on the degree of adjacent nodes, node with “friends in high places” is most central
  • Think: who has friends with the most connections?

Measures of Centrality

  • Group activity:
  • Which point(s) have the highest degree centrality? (most edges)
  • Betweenness centrality? (involved in most “shortest paths”)
  • Closeness centrality? (lowest average “shortest path” to others)
  • Eigenvalue Centrality? (“friends in high places”)

Measures of Centrality

Centrality Recap

  • Centrality is a way to measure the imporance of a network position
  • There are many different measures of centrality
  • We can choose a measure based on our theory of why centrality is important (e.g. number of edges, betweenness, closeness, friends in high places)