##           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

• 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

• 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?

• On Wednesday, we will plot networks!

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

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

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

• 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

• Community Mapping Prejudice Session: Transform Historic Racial Covenants into Actionable Data
• How does this relate to prior lessons?
• Mapping Racial Covenants
• Example Map
• The Zooniverse

• 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

• 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?

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

• Two elements:

1. Nodes
2. Edges

Network example. From Nykamp DQ, An introduction to 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.

• 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.

• 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

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

• 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’.

• One-mode vs. Two-mode

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

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

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

• 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)

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

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

• 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?

• 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?

• 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?

• 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”)

• 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)