- Tapa blanda: 304 páginas
- Editor: Morgan Kaufmann; Edición: 1 (27 de agosto de 2010)
- Idioma: Inglés
- ISBN-10: 9780123822291
- ISBN-13: 978-0123822291
- ASIN: 0123822297
- Valoración media de los clientes: 1 opinión de cliente
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- n.° 1740 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Redes y administración de sistemas
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Analyzing Social Media Networks with NodeXL: Insights from a Connected World (Inglés) Tapa blanda – 27 ago 2010
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Descripción del producto
"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."--Computing Reviews
"This is a niche book that is also multi-discliplinary. NodeXL has involved experts in information studies, computer science, sociology, human-computer interaction and cultural studies and this book has contributions from researchers in all these fields and more. It is however highly practical and will motivate readers to use this tool for their own research."--I-Programmer.info
"The authors explore the applications of Microsoft’s NodeXL, a free, open-source social network analysis (SNA) plug-in for use with Excel. It provides instant graphical representation of relationships of complex networked data, drawing on over 20 years of visual analytic theory and information visualization."--Usability News
Reseña del editor
Analyzing Social Media Networks with NodeXL offers backgrounds in information studies, computer science, and sociology. This book is divided into three parts: analyzing social media, NodeXL tutorial, and social-media network analysis case studies.
Part I provides background in the history and concepts of social media and social networks. Also included here is social network analysis, which flows from measuring, to mapping, and modeling collections of connections. The next part focuses on the detailed operation of the free and open-source NodeXL extension of Microsoft Excel, which is used in all exercises throughout this book. In the final part, each chapter presents one form of social media, such as e-mail, Twitter, Facebook, Flickr, and Youtube. In addition, there are descriptions of each system, the nature of networks when people interact, and types of analysis for identifying people, documents, groups, and events.
- Walks you through NodeXL, while explaining the theory and development behind each step, providing takeaways that can apply to any SNA
- Demonstrates how visual analytics research can be applied to SNA tools for the mass market
- Includes case studies from researchers who use NodeXL on popular networks like email, Facebook, Twitter, and wikis
- Download companion materials and resources at https://nodexl.codeplex.com/documentation
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Opiniones de clientes
Principales opiniones de clientes
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 [...].
The book also provides a nice overview of the field of social media networks. (Social media corresponds to Web 2.0 technologies.) The bulk of the book, its third part, contains eight chapters on as many different case studies. These chapters 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.
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The book has 15 chapters organised into three sections. The first three chapters serve as an introduction to social network analysis and social media like Twitter, Flickr and Facebook - none of which I use. I particularly like the chapter structure, which is consistent throughout the book. Each chapter provides key definitions and themes and finishes with both a "Practitioner's Summary" and a "Researcher's Agenda" - this means one can skim the book and quickly locate personal interests. Chapter 3 is elegant in its definitions and summary of key network analysis concepts. I particularly liked the idea of `network nirvana':
. every vertex is visible;
. every vertex's degree is countable;
. every edge can be followed from source to destination; and
. clusters and outliers are identifiable.
Section 2 is called "NodeXL Tutorial: Learning By Doing" and consists of four chapters. These chapters are, in my opinion, the heart of the book. They cover all the key elements of using NodeXL for network analysis, including layout, labelling and metrics. There is enough in the chapters to pique the interest of practitioners and academics alike, from beginner to advanced analyst. Again I like the elegance and accessibility for lay-people to some of the definitions. So much so that this text will become a standard inclusion in all my seminars and requires me to develop a NodeXL seminar and workshop!
The final section provides eight chapters, six of which are contributed by network researchers, and all of which provide case studies of network analysis using NodeXL. I found the chapter on email and lists of most interest, and commercially of most use, but from an academic perspective all the chapters are very interesting. The Twitter chapter was particularly enlightening, especially given I don't really see the value of Twitter - I'm rethinking that position at the moment! Similarly the Facebook chapter provided some interesting insights.
All in all this book was an excellent and easy read. Already my book is dog-eared and has lots of margin scribbles and highlights. I unreservedly give it five stars - five stars for readability and five stars for the learning by doing approach. It is a must-have book for every serious student and practitioner of network analysis. I can't wait for an e-book version, which I will buy immediately it is released.
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