# Exercise

Working with a partner, try to answer these questions:

• What would a network visualization in your domain look like?
• Draw up a list of the kinds of data you would need?
• What would be the source for that data?
• Can you create a sketch of the network data?
• What kinds of questions would it answer?

# Network = graph

What humanities scholars call a network, mathematicians call a graph.

A graph is defined as

• a set of nodes (or vertices) and
• a set of edges that connect those edges.

# What do the nodes and edges mean?

Nodes Edges
People Letters
People Membership
Organization People
Publications Citations
Cities Imports/exports
Documents Text borrowing
Organizations Money

# What does the data look like?

nodes
names size color
A 19 green
B 19 green
C 19 red
D 27 red
E 11 green
F 18 green
Edges
node1 node2 weight
A B 5
B C 3
D B 8
D C 6
D E 4
F D 4

# Adjacency matrix: an alternative form

``````## 6 x 6 sparse Matrix of class "dgCMatrix"
##   A B C D E F
## A . 5 . . . .
## B . . 3 8 . .
## C . . . 6 . .
## D . . . . 4 4
## E . . . . . .
## F . . . . . .``````

# Problems

• Networks are often incomplete (for example, ego networks).
• Networks are extremely difficult to visualize.
• Networks are hard to scale.
• Layouts are imposed, not inherent. Graphs can be topologically similar but layout entirely different

# Measures of networks

• Degree: how many edges does a node have? (Or, how many neighbors does a node have?)
• In-degree/out-degree: takes into account directionality
• Strength/weighted degree: degree taking into accounts weights of edges
• Betweenness centrality: nodes that could be hubs
• Closeness centrality: center of the graph
• Eigenvector centrality: nodes connected to central nodes (e.g., page rank)
• Modularity/community detection: groups of similar nodes

# Bipartite networks

• Bipartite networks have two kinds of nodes.
• Examples: members in organizations
• Bipartite networks can be projected into unipartite networks with only one type of node
• Each bipartite network will have two projections, one for each type of node.

# Exercise

Working in a group, create a network visualization using one of the provided datasets. Start by changing these properties:

• Layout
• Node size
• Node color according to modularity
• Labels