In the preface to *Big Data of Complex Networks*, the editors state in the preface that to the best of their knowledge, “this is the first book dedicated exclusively to Big data and network analysis” and that existing books treat one topic or the other separately. But this book is a collection of 12 articles by 32 authors. It’s not a *book* in the sense of being the product of a single mind who has digested the material and written a book-length presentation. The chapters of the book are specialized, though collectively they give a broader survey of large networked data.

Because the book is a collection of diverse articles, I thought it best to give a brief review of each.

The first chapter looks at biomedical data sources, including a brief survey of 43 databases, and describes a number of particular discoveries that have been accomplished in this area using network analysis.

The second chapter focuses on the architecture of distributed data analysis, in particular the computational performance and privacy issues associated with using a loosely coupled network of computers.

The third chapter reports on methods of analyzing text from Chinese social media, including the machine learning algorithms used and the computer architecture.

Chapters 4 and 12 are about visualization. The former is more static and the latter more dynamic. Chapter 4 is concerned with algorithms for graph layout, ways to create a more abstract view of graphs, and applications to security. Chapter 12 is not so much about visualizing graphs per se as visualizing changes in graphs over time.

The fifth chapter is concerned with algorithms for finding graph dominating sets (GDS). Intuitively, these are the points that dominate a graph in the sense of being influential or critical for operation.

The sixth chapter is about managing and querying data. It has more of a computer science flavor than the rest of the book.

The most mathematical chapter by far is Chapter 7. The chapter is entitled “Large Random Matrices and Big Data Analysis” though the emphasis is very much on the former, the theory of random matrices. The discussion is fairly sophisticated and may be hard for readers to follow who are not familiar with random matrices.

Chapter 8 gives an introduction to the tensions between big data and privacy law, especially in Europe.

Chapter 9 looks at applications of network analysis to neural networks. It is concerned with neural networks in the sense of literal networks of neurons and closely analogous systems. (Neural networks in machine learning are also based on an analogy with biological neural networks, though not as directly.) This chapter uses methods from statistical physics and is the most mathematical after the chapter on random matrices.

Chapter 10 is about the ScaleGraph software library for analyzing enormous graphs. It presents the software more from an engineering perspective than an user’s perspective. Much of the chapter is devoted to the parallel programming architecture of the system.>/p>

Chapter 11 is entitled “Challenges of Computational Network Analysis with R”. A reader might think this refers to the R programming language, but it does not. The article mentioned many programming languages and technologies, but the R language is not one of them. The R in the title refers to a measure of graph robustness, how well a graph remains connected as the most connected nodes are removed.

In summary, *Big Data of Complex Networks* contains articles that span a wide variety of perspectives and applications.

John D. Cook is an independent consultant working in applied mathematics, statistics, and computing.