In Detail
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.
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.
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.
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.
Approach
A practical, hands-on guide, that provides you with all the tools you need to visualize and analyze your data using network graphs with Gephi
Who this book is for
This book is for data analysts who want to intuitively reveal patterns and trends, highlight outliers, and tell stories with their data using Gephi. It is great for anyone looking to explore interactions within network datasets, whether the data comes from social media or elsewhere. It is also a valuable resource for those seeking to learn more about Gephi without being overwhelmed by technical details.
