Complex networks, that is, networks with complex topological features, are ubiquitous in almost every branch of science and technology. As a result, the study of complex networks has become a significant focus of attention in various disciplines. This book focuses on the algorithmic aspects of complex network analysis and makes an effort to provide the basic theoretical and algorithmic knowledge for researchers and students of this interesting topic.

The book is divided into three main sections. In the first section, comprising three chapters, the essential backgrounds on graph theory, algorithmic analysis, and complexity as well as some preliminaries on network analysis are presented. After a nice introduction in chapter 1, the author presents basic graph-theoretic concepts in chapter 2. Chapter 3 makes a good effort to provide background on algorithms and complexity analysis. This is the largest chapter in this section and possibly the second largest in the whole book, and it briefly covers different basic complexity notions, different algorithmic techniques, complexity classes, approximation algorithms, and so on. The author also very briefly discusses parallel and distributed settings. As expected, this chapter covers many topics but only briefly. Finally, in chapter 4, the author discusses fundamental indexes and matrices used in the literature to analyze a network. Overall, this section of the book quite nicely sets up a reader for what is coming next.

The second section can be seen as the heart of this book, and it consists of five chapters. The author begins this section by discussing different measures for distance and centrality for complex networks in chapter 5. Then, in chapter 6, the author again returns to graph theory and discusses a number of known graph structures and related algorithms that are believed to be interesting from a complex network point of view. Chapters 7 and 8 deal with a very important concept called clustering. The author divides the discussion on clustering into two chapters: in chapter 7, he discusses data clustering, and in chapter 8, he again returns to graphs and discusses graph clustering. Finally, in chapter 9, the concept of motifs and the algorithms to discover such interesting structures are presented.

Perhaps the most interesting section of the book is the third section, where the author focuses on applications and case studies. This section comprises four chapters with four application scenarios. Biological networks are discussed in chapter 10. Then, in chapter 11, social networks are considered briefly, followed by the Internet and World Wide Web in chapter 12. Finally, the book ends with a brief chapter on wireless ad hoc networks.

Overall, the book can be seen as a nice introductory book on complex networks. In fact, the back cover does indicate that this is for beginners in the field. To this end, the book does a good job. Each chapter has examples to explain the algorithms, and the relevant pseudocode is provided. An excellent feature of the book is the addition of chapter-wise exercises that will definitely help beginners. Perhaps the author should consider maintaining a website with solutions to the problems and provide new problems and solutions as time progresses. I enjoyed reading the book, but some advanced researchers with computing science backgrounds may not be content with the materials presented.

One thing that is perhaps missing is that the applications are not really discussed at all before Section 3. While a separate section on applications is an excellent addition to the book, I would have preferred to have some brief discussions on the applications interleaved within the chapters to motivate the readers a bit more. Finally, the book suffers from some mistakes, such as misreferenced figures and/or sections and slightly erroneous equations, that should be fixed in the next edition.