Networks in Gephi
Activities for February 20-Get the Data Ready
You may choose to work along or with a partner. Read through the defintions below, examine Jessica Mills’s sample spreadsheets on Catherine of Sienna (available on Bb), and then make your own nodes and edges files using either Gephi’s data laboratory or an Excel spreadsheet that you then import. **Note: I’ve found that the Excel spreadsheets in Bb work best when you right click on them and then “Download linked file.” They are easier to read when viewing them in Excel than in their csv format.
I’ve written some definitions below that may help you as you wade through the tutorials. I’ve found that this tutorial is the friendliest place to start. You can use Jessica’s files to run through the tutorial one time on your own to get the hang of how the program works.
If you are interested in making dynamic, timebased networks, check out this tutorial.
Requirements by end of class
- Nodes spreadsheet that includes id, label, and attributes of useful information that is graphable.
- Edges spreadsheet that includes source, target, type, id, and weight
Activities for February25 -Visualize
Work on making the details of your data come to fruition on the visualization. You should use the customizing features in Gephi to demonstrate the relationships you are visualizing and explain their relative importance. This post on refugees by Martin Grandjean may help you understand what the visual elements of the graph can convey.
This tutorial is the best for learning basic visualization skills.
Definitions
Nodes-the objects whose relationships you’ll be defining in the analysis (i.e. characters, locations, communication technologies).
Edges-The line in the analysis that appears between nodes signifying the relationship between them.
Source-When defining edges, the source is the unique identifier for the node at which a relationship originates.
Target-When defining edges, the target is the node with which a source forms a relationship.
Directed vs. Undirected-When defining edges, if a relationship starts at a source and goes to a specific target it is directed. Otherwise, if the relationship goes both ways and is mutual, it is undirected.
Weight-In the edges file, you can establish a weight column that will allow you to visualize the strength of a connection between the nodes. For example, if you were graphing the number of times a character found herself in Dracula’s presence, Jonathan would have a higher weight (measured by the number of times) than Mina.
Eigenvector Centrality-“In graph theory, eigenvector centrality (also called eigencentrality) is a measure of the influence of a node in a network. It assigns relative scores to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes” (from Wikipedia).
Attribute-Additional characteristics that you assign a node in your spreadsheet. You might, for example, define relationships and then give relationships unique colors in the resulting graph.