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Gephi Tutorial: An Introduction To Using The Program For Network Analysis

Gephi Tutorial: An Introduction To Using The Program For Network Analysis

How To Conduct Network Analysis Using Gephi?

We have created a straightforward Gephi tutorial to assist you in performing organizational network analysis on a sample dataset if you’d like to get your hands dirty with some ONA software. By using your context knowledge to understand better what network statistics mean for the organization, you can better understand the logic of the analysis, the opportunities and limitations offered by this open-source software, and a more meaningful interpretation of results when you conduct the analysis yourself.

Describe Gephi.        

One of the most widely used pieces of open-source network analysis software is Gephi. It is praised in particular for the network visualizations it can create, ranging from stunning network art to highly complex network graphs.

Why is Gephi Useful?

Gephi is a good network analysis tool for beginners. The point-and-click program can handle simple and complex network analytics and customizable graph visualizations (through plugins). On static and dynamic networks, geo-located data, and multimode/multiplex networks, one can design network graphs and analyze network structures, communities, and critical actors. Gephi has fewer analytic capabilities than programming-related software like R or Python. The most advanced software toolkit for descriptive and inferential analyses is currently network analysis in R.

Open Source Network

Gephi tutorial is a fantastic open-source network analysis and (interactive!) visualization program with many beneficial tools for perusing graph data, computing statistics, finding clusters and communities, etc. No knowledge of coding is necessary. Please stop using Python’s NetworkX for a while and try Gephi instead, even if you are a coder. It is worthwhile.

There are several different layout algorithms that Gephi offers, which affect how the graph display in real time. Let’s test them to the test to determine their logic and the benefits and drawbacks of each.

This StackOverflow dataset, obtained from Kaggle, will be used. I designed a network with people acting as nodes, with an A—B link only existing when A response to B’s query. The volume of the response is the edge. I used the Python pandas library to make this.

How To Add Information in Gephi?

Add some more data now.

The “Data Laboratory” link will appear; click on it to upload your data.

And first, select “Import Spreadsheet.”

Import both the “nodes.csv” and “edges.csv” files. I will provide you with sample edges and node files if you attend my workshop on “Introduction to Network Analysis.” Here is some test data you can use to test this out asynchronously. Read on if you’re using your data:

A node table and an edge table must be made as two.csv files. Then excel files automatically save as the.xlsx format by default. When you click “Save as,” save the file as.csv to obtain the.csv format.

Here are some general distinctions between nodes and edges.

Nodes: Gephi tutorial  is informed of every potential node in a network by the nodes file. In the Gephi visualization, a node is represent by a circle, and the edges file informs Gephi of the relationships between each node (or connects). Then Id and Label should be the only columns in the nodes file. The Id in our illustration is a number (e.g., 1, 2, 3, 4, 5). The Label is what you want to appear for the node in the actual graph. And the attributes may also be included in the node table. By classifying your data according to color, size, or age, attributes allow you to distinguish between your nodes.

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Edges: Gephi tutorial informs how the nodes is connect by the edges table (the second.csv table). The Source, Target, and Type columns are present. A node you have located and assign a label is refer to as a source. csv document Target can also be a node specified in your nodes. csv document Type describes the connection between the two nodes. A relationship is consider “Directed” if the source (such as the sender of a letter as opposed to the recipient) controls it. In this illustration, the letter’s source is the sender of the object. The graph will be undirected if the relationship is reciprocal, such as when the graph represents friendships. Other than this trending topic, art of zoo is also a top trend on social media these days.

How To Use Your Graph To Gather Data

You can explore and visualize your data in the following ways:


Gephi modifies the network’s nodes and edges using its layout feature. It gives different network properties priority.

From the drop-down menu, select a layout (e.g., ForceAtlas 2)

Adapt the layout algorithm’s parameters.

Select “Run” from the menu.

Until you are satisfied with the outcome, keep improving the layout.


Choose a “partition” (definite) node variable from your data. For instance, the variable “State” appears in our sample data from the Gephi workshop.

Toggle to “Partition”

Then select “Nodes”

Pick “State” from the drop-down menu.

And choose “Apply”


On the right, click the “Filters” tab.

Enlarge the “Attributes” directory

Click the “Equal” folder twice.

Drag “sex” to the questions section below.

Select “Filter” from the menu.


Uniformly resize nodes

On the left vertical toolbar, click the selection box icon.

To select all nodes, enclose them in a box.

On the vertical toolbar on the left, select the diamond icon.

Drag the mouse up and down to increase and decrease the size after clicking on a node.

Nodes are resized based on a numerical variable.

Select “Ranking” from the menu.

Select “Nodes”

And Choose a variable from the drop-down menu, such as Degree.

Decide on a minimum and maximum size range for the nodes.

Select “Apply” from the menu.