Networks in this context are collections of nodes connected by edges, networks are ubiquitous. The nodes may be people in a social network, and the edges their friendships. Or the nodes might be proteins and metabolic products and the edges the reaction pathways between them. Or any other of a multitude of systems. I’ve reviewed a couple of other books in this area including Barabási’s popular account of the pervasiveness of networks, Linked, and van Steen’s undergraduate textbook, Graph Theory and Complex Networks, which cover the maths of network (or graph) theory in some detail.

Mastering Gephi is a practical guide to using the Gephi Network visualisation software, it covers the more theoretical material regarding networks in a peripheral fashion. Gephi is the most popular open source network visualisation system of which I’m aware, it is well-featured and under active development. Many of the network visualisations you see of, for example, twitter social networks, will have been generated using Gephi. It is a pretty complex piece of software, and if you don’t want to rely on information on the web, or taught courses then Cherven’s books are pretty much your only alternative.

The core chapters are on layouts, filters, statistics, segmenting and partitioning, and dynamic networks. Outside this there are some more general chapters, including one on exporting visualisations and an odd one on “network patterns” which introduced diffusion and contagion in networks but then didn’t go much further.

I found the layouts chapter particularly useful, it’s a review of the various layout algorithms available. In most cases there is no “correct” way of drawing a network on a 2D canvas, layout algorithms are designed to distribute nodes and edges on a canvas to enable the viewer to gain understanding of the network they represent.  From this chapter I discovered the directed acyclic graph (DAG) layout which can be downloaded as a Gephi plugin. Tip: I had to go search this plugin out manually in the Gephi Marketplace, it didn’t get installed when I indiscriminately tried to install all plugins. The DAG layout is good for showing tree structures such as organisational diagrams.

I learnt of the “Chinese Whispers” and “Markov clustering” algorithms for identifying clusters within a network in the chapter on segmenting and partitioning. These algorithms are not covered in detail but sufficient information is provided that you can try them out on a network of your choice, and go look up more information on their implementation if desired. The filtering chapter is very much about the mechanics of how to do a thing in Gephi (filter a network to show a subset of nodes), whilst the statistics chapter is more about the range of network statistical measures known in the literature.

I was aware of the ability of Gephi to show dynamic networks, ones that evolved over time, but had never experimented with this functionality. Cherven’s book provides an overview of this functionality using data from baseball as an example. The example datasets are quite appealing, they include social networks in schools, baseball, and jazz musicians. I suspect they are standard examples in the network literature, but this is no bad thing.

The book follows the advice that my old PhD supervisor gave me on giving presentations: tell the audience what you are go to tell them, tell them and then tell them what you told them. This works well for the limited time available in a spoken presentation, repetition helps the audience remember, but it feels a bit like overkill in a book. In a book we can flick back to remind us what was written earlier.

It’s a bit frustrating that the book is printed in black and white, particularly at the point where we are asked to admire the blue and yellow parts of a network visualisation! The referencing is a little erratic with a list of books appearing in the bibliography but references to some of the detail of algorithms only found in the text.

I’m happy to recommend this book as a solid overview of Gephi for those that prefer to learn from dead tree, such as myself. It has good coverage of Gephi features, and some interesting examples. In places it is a little shallow and repetitive.

The publisher sent me this book, free of charge, for review.

Gephi is a system for producing network visualisations, as such it doesn’t have a great many competitors. Fans of Unix will have used Graphviz for this purpose in the past but Gephi offers greater flexibility in a more user-friendly package. Graph theory and network analysis have been growing in importance over the past few years in part because of developments in the analysis of various complex systems using network science. As a physical scientist I’ve been aware of this trend, and it clearly also holds in the social sciences. Furthermore there is much increased availability of network information from social media such as Twitter and Facebook.

I’ve used Gephi a few times in the past, and to be honest there has been an air of desperate button clicking to my activities. That’s to say I felt Gephi could provide the desired output but I could only achieve it by accident. I have an old-fashioned enthusiasm for books even for learning about modern technology. Hence Network Graph Analysis and visualization with Gephi – the only book I could find with Gephi in the title. There is substantial online material to support Gephi but I hoped that this book would give me a better insight into how Gephi worked and some wider understand of graph theory and network analysis.

On the positive side I now have a good understanding of the superficial side of the interface, a feel for how a more expert user thinks about Gephi, and some tricks to try.

I discovered from Network Graph Analysis that the “Overview” view in Gephi is what you might call “Draft”, a place to prepare visualisations which allows detailed interaction. And the “Preview” view is what you might call “Production”, a place where you make a final, beautiful version of your visualisations.

The workflow for Gephi is to import data and then build a visualisation using one of a wide range of layout algorithms. For example, force-based layouts assume varying forces between nodes for which an arrangement of nodes can be calculated by carrying out a pseudo-physical simulations. These algorithms can take a while to converge, and may get trapped in local minima. The effect of these layout algorithms is to reveal some features of the network. For example, the force layouts can reveal clusters of nodes which might also be discovered by a more conventional statistical clustering algorithm. The concentric layout allows a clearer visualisation of hierarchy in a network.

It’s clear that the plugin ecosystem is important to the more experienced user of Gephi. Plugins provide layout algorithms, data helpers, new import and export functionality, analysis and so forth. You can explore them in the Gephi marketplace.

Cherven recommends a fairly small, apparently well-chosen set of references to online resources and books. The Visual Complexity website looks fabulous. You can read the author’s complete, pre-publication draft of Networks, Crowds and Markets: Reasoning about a highly connected world by David Easley and Jon Kleinberg here. It looks good but it’s nearly 800 pages! I’ve opted for the rather shorter Graph Theory and Complex Networks: An Introduction by Maarten van Steen.

On the less positive side, this is an exceedingly short book. I read it in a couple of 40 minute train journeys. It’s padded with detailed descriptions of how to install Gephi and plugins, including lots of screenshots. The coverage is superficial, so whilst features may be introduced the explanation often tails off into “…and you can explore this feature on your own”.

Network Graph Analysis is disappointing, it does bring a little enlightenment to a new user of Gephi but not very much. A better book would have provided an introduction to network and graph analysis with Gephi the tool to provide practical experience and examples, in the manner that Data Mining does for weka and Natural Language Processing with Python does for the nltk library.

This book may be suitable for someone who is thinking about using Gephi and isn’t very confident about getting started. The best alternative that I’ve found is the online material on GitHub (here).