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?

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.

Nodes | Edges |
---|---|

People | Letters |

People | Membership |

Organization | People |

Publications | Citations |

Cities | Roads/canals/railways |

Cities | Imports/exports |

Documents | Text borrowing |

Organizations | Money |

names | size | color |
---|---|---|

A | 19 | green |

B | 19 | green |

C | 19 | red |

D | 27 | red |

E | 11 | green |

F | 18 | green |

node1 | node2 | weight |
---|---|---|

A | B | 5 |

B | C | 3 |

D | B | 8 |

D | C | 6 |

D | E | 4 |

F | D | 4 |

```
## 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 . . . . . .
```

- 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

- 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 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.

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