Book Image

Network Graph Analysis and Visualization with Gephi

By : Ken Cherven
Book Image

Network Graph Analysis and Visualization with Gephi

By: Ken Cherven

Overview of this book

<p>Gephi is an interactive visualization and exploration platform for all kinds of networks and complex systems. Social media data has helped to drive network visualization to new levels of relevance and importance. However, there is far more to network visualization than just social media data. For analyzing and visualizing network graphs, you need to have an excellent platform, and you need to know ways to use your data effectively.</p> <p>Network Graph Analysis and Visualization with Gephi is a practical, hands-on guide that provides you with all the tools you need to begin creating your own network graphs. You will learn how to import data, test multiple graph layouts, and publish your visualizations to the Web.</p> <p>Network Graph Analysis and Visualization with Gephi will teach you how to create your own network graphs using Gephi. The book begins by taking you through the installation of Gephi and configuring the installation options. You will also get acquainted with the Gephi workspace and the various tools in Gephi. Next, you’ll use these tools to create your own graphs. If you need to add more capability to your personal toolkit, you will be learning to Download and install several of the best Gephi layout plugins. You will then use these layouts simultaneously to produce beautiful graphs. Also, you create and import data in Gephi and add some new plugins that extend Gephi even further. You also gain the skills to prepare and customize your network visualization for export.</p> <p>By the end of this book, you will be able to create your own network graphs using Gephi, customize the look and feel of your graphs, and successfully publish them to the Web.</p>
Table of Contents (16 chapters)

Finding the most effective layout


I hope you're beginning to get a feel of how to detect visually when a model is visually and analytically effective. One of the keys to getting to this point is to test multiple methods before deciding on a final choice. For example, we might have felt perfectly good about using the OpenOrd graph with our school data if we hadn't experimented with other algorithms. For this data, we would certainly select another model, because we were able to see the results from each approach.

Equally important is to consider how we are trying to frame the data and ultimately our graphical output. We should always ask ourselves a few questions before settling on a final choice:

  • Am I trying to provide an overview of the entire network, or is my goal to focus on a specific node and its relationships?

  • What is more critical to my display – showing the sheer number of connections between nodes or their intensity (frequency)?

  • Are there groups in my data that should be treated as...