In this chapter, you witnessed a variety of approaches that will help you to tell better stories using Gephi and network graphs. Here's a brief recap of the five approaches we examined.

We began with partitioning, and learned how to use both existing data attributes as well as calculated values. Our next step was to illustrate the use of ranking in sizing and coloring graphs for visual impact.

Manual editing of graphs was also explained, where we used filtering to make this process both more effective and efficient to highlight portions of a graph.

The Chinese Whispers plugin was used in a series of examples to illustrate the power of applying clustering methods to a graph.

We concluded with a series of illustrations using the Markov Clustering method and learned how to adjust settings to improve our graph output.

In our next chapter, Chapter 8, Dynamic Networks, we'll begin to look at how to create time-based visualizations using Gephi. We'll also learn how to create time intervals and use existing ones to create dynamic network graphs, and begin to understand how to implement individual node and edge values that change over time.