Writing simple, introductory books on complex subjects such as Network Analysis is a difficult task, and one that Ken Cherven performs beautifully.
This book is a good introduction to the topic for somebody who needs to get the general idea (say, a data science manager’s manager) without actually having to produce a graph analysis and interpret its results (no, the two tasks cannot be performed by separate actors).
Beginning network scientists can also use the book as a Gephi primer.
Although network analysis and visualization are complex disciplines, the author succeeds in providing a good overview of why they are important and what answers they can provide, using many practical examples from a well-chosen, easy-to-understand dataset.
The author also succeeds in guiding a beginner around Gephi’s interface that, although well-designed and structured, doesn’t hide the complexity of graph analysis.
I would have liked to see a little more detail given to the fundamental definitions of graph analysis: betweenness centrality, closeness centrality, degree, etc. Instead, they make a fleeting appearance at the end of the “Advanced Features” chapter. On the positive side, they are reasonably if rapidly illustrated with examples; still, I think they would have deserved much more attention.
Also, the index is very limited and of little practical use, a defect that could be easily avoided in e-books.
Still, Network Graph Analysis and Visualization with Gephi is a good introduction to a very complex discipline that is justly experiencing explosive growth.
I give it 3 stars (actually, 3+) rather than 4, only because its few drawbacks could have been easily avoided.