Visual Complexity: a review of Manuel Lima’s new book

Until August 30, 2009, I knew little about Manual Lima and his work beyond the fact that he ran the data visualization website When he published his “Information Visualization Manifesto” on that day, however, I recognized him as a kindred spirit: someone who believed that data visualizations should be designed to enlighten. When I recently heard that he had written a book, Visual Complexity: Mapping Patterns of Information, I was eager to read it. I finally had my chance, and here are my thoughts.

It’s important to recognize up front that this book is not about data visualization in general but about network visualization in particular. This is also the focus of, where Lima showcases hundreds of network visualizations. If you share his intense fascination with networks (their nature, ubiquity, and complex beauty) and the many ways that networks can be represented graphically (various display approaches, the history of their development, and their potential as art) you will probably enjoy the intellectual meanderings in this book, which ventures at times into philosophical speculation. However, if you want to understand how network visualizations work, what makes them effective, when to use one approach rather than another, or how well the many examples in this book perform as vehicles of insight, you will be disappointed.

I believe that the merits of a book should be judged by how well it achieves what the author promises. Authors of a non-fiction work such as this should always declare their objectives­ and do their best to fulfill them. In the introduction to Visual Complexity, two of the ways that Lima characterizes the book are not satisfactorily delivered: 1) he says that the book “looks at the depiction of networks from a practical and functional perspective,” and 2) he describes it as a “comprehensive study of network visualization [that] should ultimately be accessible to anyone interested in the field, independent of their level of expertise or academic dexterity.” Given the focus of my work, I was particularly interested in the book’s ability to live up to these two goals.

In a short section of the third chapter, Lima presents a few principles for the design of network visualizations, but he never applies those principles to the visualizations that appear throughout the book. How can we learn from those examples, many of which are incomprehensible given the brief descriptions that accompany them, without an explanation of the insights that they pursue and a critique of their effectiveness in capturing and revealing those insights? Network visualizations are notoriously difficult to fathom, often looking like giant hairballs of complexity. Even when they’re well designed, they usually require instruction and practice to decipher. A comprehensive treatment of network visualization must do more than showcase examples; it must help us fathom the depths.

In a section of the book that I found helpful, Lima categorizes network visualizations by differences in form (arc diagrams, area groupings, centralized burst, etc.), but makes no attempt to describe their various strengths, weaknesses, or appropriate uses. When we should select one form instead of another is never hinted.

Lima exhibits many network visualizations, breaks them into categories, and provides a wee bit of guidance, but spends most of the book’s 257 pages delving into history, philosophy, science, and art with the erudition of a museum curator. The breadth of his knowledge is impressive, spanning several fields, which he weaves into an interdisciplinary network of ideas. Academics in the field will find his tour thought provoking. While interesting, however, it feels like an intellectual exercise with no bridge to the real world. More and more today we need to understand networks, from the microscopic world of neurons in our brains to the macroscopic realm of social movements and the World Wide Web. I kept looking for content in this book that I could apply to these challenges in practical ways, but found little.

In the chapter titled “Complex Beauty,” Lima speculates about the causes of our attraction to “depictions of complex networks.” I found his speculation interesting, but couldn’t help wondering if its premise were indeed true. Are people naturally attracted to complex network visualizations? Who comprises the “we” that experiences this allure? I suspect that network visualizations are alluring to people like Lima and me who work in the field, but few others.

The final chapter of the book, “Looking Ahead,” seems out of place, a misfit as the book’s finale. It consists of four essays by others working in the field, but the topics of these essays don’t focus on network visualization and in two cases don’t deal with networks at all. Each essay is thoughtful but only peripherally relevant to the book.

Those with expertise in network visualizations will find this book engaging; a worthwhile addition to their library. Those looking for a comprehensive, accessible, and practical guide to network visualization must prolong the wait. Perhaps Lima will provide this book in the future. He is perhaps better qualified than anyone else. If so, I invite him to descend from the lofty heights of aesthetic musings to the realm where real networks wait to be revealed.

Take care,

5 Comments on “Visual Complexity: a review of Manuel Lima’s new book”

By jonmcrawford. November 17th, 2011 at 12:36 pm

Do you have a recommendation other than this book for network visualizations that might include practical application of concepts?


By Joe Mako. November 17th, 2011 at 2:37 pm

Have you seen the style of network visualizations called Hive Plots:

There is a nice poster at the bottom of the page:

I think the Hive Plot Trellis style view is better than conventional network layouts, and becomes more effective with more complexity in my opinion.

By Stephen Few. November 18th, 2011 at 2:11 pm

Hi Joe,

I don’t believe that I’ve run across Hive Plots before. I’ll take some time to look them over closely when I return from Australia. Do you think a Hive Plot might have been more effective for telling the story in the New York Times?

By Stephen Few. November 18th, 2011 at 2:14 pm


Unfortunately, I’m away from my library right now, and no books that cover the practical use of network diagrams comes to mind. It’s possible that the book that Ben Shneiderman coauthored not long ago about the network node-link visualization add-in for Excel named NodeXL approaches the topic in a practical way, so you might search for some information about it.

By Joe Mako. November 19th, 2011 at 4:14 am

I do not think a Hive Plot would be effective alternative by itself for the New York Times story discussed at

The Hive Plot shows pairs and tiers, and does not show size or direction. The Hive Plot could only shows part of the story, so it may fit in as one of multiple charts to tell the story. The downside is the viewer must first study a resource like the poster I linked to understand what the Hive Plot is showing. So while it would be an interesting thing to try, I do not think it would replace the NYT graphic.