2. Open your application.
3. Click on "New Project" on the "Welcome to Gephi" popup window.
Additional resources as you troubleshoot installation:
Official Gephi learning portal
Here is a fifteen minute video offering an overview of how to use Gephi.
Click on "Data Laboratory" This is where you'll upload your data.
1. Click on "Import Spreadsheet."
2. Import the "edges.csv" file and the "nodes.csv" file. If you come to my workshop "Introduction to Network Analysis," I will give you sample edges and nodes files. If you are trying this out asynchronously, here is some sample data to use. If you're using your own data, read below:
You will need to create two .csv files: a node table and an edge table. Excel files automatically save by default as .xlsx format. In order to get the .csv format, save the file as .csv when you click "Save as."
In general, here is a bit about the difference between nodes and edges:
Nodes: the nodes file tells Gephi all the possible nodes in a network. A node is represented by a circle within the Gephi visualization whereas the edges file tells Gephi how all the nodes are related (or connected). The nodes file should at least have the columns "Id" and "Label." In our example, the Id is a number (e.g. 1, 2, 3, 4, 5). The label is what you want to see the node labeled as in the graph itself. Your nodes table might look like this:
The node table can also include attributes. Attributes offer a way for you to distinguish between your nodes by categorizing your data by, for example, color, size, or age.
Edges: The edges table (the second .csv table) tells Gephi how the nodes are connected. It has the columns Source, Target, and Type. Source refers to a node that you've identified and labeled in your nodes.csv file. Target also refers to a node you've listed in your nodes.csv file. Type refers to how the two nodes are connected. If the source drives the relationship (for example, a sender of a letter versus a receiver), the relationship is "Directed." In this example, the sender of the letter is the source and the receiver of the letter is the target. If the relationship goes both ways -- for example, the graph visualizes friendships, the graph will be undirected. Here is an example of what your graph will look like:
In the edges table, you can also add a column to define the weightedness for each relationship. Weight gives you the option to show the importance of certain relationships by giving them a numerical weight.
After uploading both your nodes and edges table to Gephi, you'll need to tell Gephi you'd like a column for labels. To do that, click on ID and then click on Label. Then, finally, click "save." Now we can start visualizing!
Click on "Overview" to see your graph.
If you do not see your graph, click on "window" and then click "graph." This will populate a window for your graph.
What is that?
The initial visualization you see won't look like much, but don't worry! There are three ways we can explore and improve this graph:
1. Overview: We can explore the graph visually
2. Data Laboratory: This is where we can see the spreadsheet view of our data -- and the new data we create as we analyze our graph
3. Preview: This is where we polish our visualization.
Here are a few ways you can explore and visualize your data:
Layout
Gephi adjusts the nodes and edges in the network by the layout feature. It prioritizes different properties of the network.
Choose a layout from the drop-down list (e.g., ForceAtlas 2)
Adjust parameters for the layout algorithm
Click the "Run" button
Continue to refine the layout until you are happy with the results
Color
Select a "partition" (categorical) node variable from your data. For example, in our sample data in the Gephi workshop we have the variable called "State"
Click on "Partition"
Click on "Nodes"
Choose "State" from the drop down
Click "Apply"
Filter
Click the "Filters" tab on the right
Expand the "Attributes" folder
Double-click the "Equal" folder
Drag “sex” down to the “Queries” below.
Click the "Filter" button
Size
Resize nodes uniformly
Click on the selection box icon on the left vertical toolbar
Draw a box around all nodes to select them all
Click on the diamond icon on the left vertical toolbar
Click on a node, then drag the mouse up and down to increase and decrease the size
Resize nodes according to a numerical variable
Click on the "Ranking" tab
Click on "Nodes"
Select a variable (e.g., Degree) from the drop down
Choose a minimum and maximum size as a range for the size of the nodes
Click the "Apply" button
Statistics
Click the Statistics tab on the right hand side
Run the “modularity” statistic as a first example.
This creates a new way to view your graph. It also populates a new cell in your data laboratory.
Click into the “Appearance” tab on the left-hand side. Under “nodes” click “modularity class” in the “Partition” tab.
Color your nodes by community
Once you've calculated modularity, we can color nodes according to their communities. Go to the Partition pane (on the left side of the Gephi window) and click on the little Refresh icon. From the dropdown window, select Modularity Class.
Now the color of your nodes will change according to the Modularity Class.
If you'd like to open your file again in Gephi, you can save it as a Gephi file.
You can also take a screenshot from the Overview panel by clicking on the camera.
When you're satisfied with your image, you can export the file as an SVG/PDF/PNG file.
Here are a few tutorials on Gephi that I find helpful:
Clément Levallois's tutorial: https://seinecle.github.io/gephi-tutorials/generated-html/simple-project-from-a-to-z-en.html
Brian Sarnacki's Gephi Tutorial: http://www.briansarnacki.com/gephi-tutorial/
Martin Grandjean's Gephi Introduction: http://www.martingrandjean.ch/gephi-introduction/
Miriam Posner's Creating a Network Graphi with Gephi: http://miriamposner.com/dh101f14/wp-content/uploads/2014/11/Creating-a-Network-Graph-with-Gephi.pdf
There are a variety of other network analysis and visualization tools available that may (or may not) fit your needs better than Gephi:
1) Cytoscape: https://cytoscape.org/
2) D3.js: https://d3js.org/
3) Palladio: https://hdlab.stanford.edu/palladio/
4) Nodegoat: https://nodegoat.net/
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