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Reviewer: Fernando Berzal

For the last dozen years, networks have attracted the attention of many researchers and practitioners. Some simple network models [1,2] have achieved significant popularity beyond Andy Warhol's 15 minutes of fame. Such models have spurred the development of a whole new field of research that combines graph theory, computer science, economics, and other social sciences. The generated momentum has led to the recent publication of some outstanding textbooks on networks [3,4,5]. More than a classical textbook, this book is a well-written educational handbook that describes how we can apply part of the so-called science of networks to the analysis of medium-size networks. The authors impose the size constraint because of the book's reliance on visual inspection and interactive visualization techniques (sometimes referred to as visual analytics). Since the available space on a screen is severely limited, one cannot expect to analyze a billion-node network using the tool described in this book. The book also provides a nice overview of the field of social media networks. (Social media corresponds to Web 2.0 technologies.) A surprising feature of this book, especially when compared with its more formal counterparts, is that the authors avoid any kind of mathematical formalization of the topics they discuss. They seem to have followed Stephen Hawking's advice: "Someone told me that each equation I included in the book would halve the sales" [6]. In fact, many readers will be grateful to learn that the authors do not include a single mathematical equation, not even to define important network metrics from an intuitive point of view. (Stephen Hawking did include a single equation: Einstein's famous E = mc 2.) However, some chapters include "Advanced Topics" sidebars that describe how to get the most out of Excel when using the NodeXL tool. NodeXL is a social network analysis open-source plug-in for Excel 2007/2010. It lowers the barrier of entry that is typical of many other network analysis tools; anyone with some experience using spreadsheets can use it. NodeXL computes some of the best-known network metrics, and, with the help of some Excel features, makes network filtering and visualization a breeze. It also implements different network layout algorithms and clustering techniques to discover natural groups of nodes within a complex network. The book only mentions one clustering technique, but more alternatives are available by downloading the latest version of the software from the NodeXL homepage (http://nodexl.codeplex.com/). The introductory part of the book goes through some of the history and core concepts of social media and social network analysis, always with an eye to social network visualization and for a general audience-it omits technical details. Later, it proposes a framework for categorizing different collaboration technologies and describes 11 categories as examples. The second part of the book features a variety of tool details. It presents an eight-hour tutorial that surveys NodeXL's main features. The bulk of the book, however, is the third part, which contains eight chapters on as many different case studies. These chapters, which are often by international researchers on social media, focus on different kinds of social networks and follow a more or less standard structure. They cover email, threaded communications, Twitter, Facebook, organizational World Wide Web (WWW) networks, Flickr, YouTube, and wikis. The chapters describe the particularities of each technology and present information on the types of networks one can analyze, from Facebook ego networks to the interplay between content and community networks on Flickr or YouTube (that is, the content provided by tag clouds and video networks versus the structure provided by user networks, where you can explore each user's followers and/or friends). Each chapter concludes with some observations for practitioners, an agenda for researchers, and a good selection of relevant bibliographic references for each case study. Ideally, these analyses should provide some useful insights and a good idea of what types of questions one can ask (and answer using social network analysis) about individuals, groups, temporal evolution, and structural patterns in social media networks. In summary, Hansen, Shneiderman, and Smith, along with their collaborators, have written a readable introduction to the field of social media network analysis. Furthermore, the book is a nice tutorial on an interesting tool that readers can experiment with on their own. For example, the readers can simply use the Facebook application provided by Bernie Hogan, one of the book's collaborators, to analyze the ego networks that they know best: their own network of friends, family, and acquaintances. This book offers a sure way to understand some of the basic concepts of network analysis. Online Computing Reviews Service

Reviewer: Symeon D. Retalis

Recent statistics mention that 1.2 billion users worldwide now log on to social networks [1]. With the rapid expansion of the use of social networking environments, researchers have been trying to analyze and interpret multiple dimensions of user interactions in these environments. There are several freeware, open-source, and commercial tools available to analyze users' online behavior and interactions, as well as their relationships with their peers, which constitute a social network. This thorough and practical guide, written by experts in information studies, computer science, sociology, human-computer interaction, and cultural studies, presents the NodeXL tool, a free and open add-in for Excel. Its 15 chapters are grouped into three main parts: an introduction about social media and social network analysis; a brief tutorial of the NodeXL tool; and a practical example of how to use the tool for analyzing different social media systems, such as email, YouTube, and wikis, using built-in layout algorithms. This is the only book in the field that provides step-by-step practical instructions on how to make the most of the tool when trying to analyze live data from social networking environments such as Facebook and Twitter. It is a great book for novices in social networking analysis and researchers who want guidance on how to analyze data from a specific type of social medium. The only shortcoming is that the authors don't provide a thorough comparison of the various available social network analysis (SNA) tools. Thus, although NodeXL is a usable tool with many features, the reader cannot easily understand its pros and cons when compared with other tools. It would have been interesting if the authors had included more domain-specific cases. For example, they could have included a case from online education. Educators in networked learning communities receive very little support from tools to evaluate the flow of students' learning activities and examine learners' online behavior. An example of how NodeXL could be used to help educators analyze and interpret multiple dimensions of interaction in networked learning communities could be very interesting. The domain of interaction analysis in online education (educational data mining or learning analytics) has attracted a lot of attention lately [2]. This book can be valuable to instructors who want to teach SNA using NodeXL. Each chapter has an outline, an introduction, a practitioner's summary, and a researcher's agenda, which is very useful in teaching. In summary, this is an excellent book for both SNA novices and SNA practitioners and researchers who need clear theoretical and practical ideas about the way to analyze and interpret data coming from social networking environments. Online Computing Reviews Service