Logo - springer
Slogan - springer

Computer Science - Information Systems and Applications | Network Analysis Literacy - A Practical Approach to Network Analysis Project Design

Network Analysis Literacy

A Practical Approach to Network Analysis Project Design

Universität Heidelberg, Dr. D. Universität Heidelberg

2014, 400 p. 60 illus., 10 illus. in color.

Available Formats:

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.


ISBN 978-3-7091-0741-6

digitally watermarked, no DRM

The eBook version of this title will be available soon

learn more about Springer eBooks

add to marked items


Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.


(net) price for USA

ISBN 978-3-7091-0740-9

free shipping for individuals worldwide

Due: August 29, 2014

add to marked items

  • About this book

  • teaches users how to pose a defined network analytic questions
  • shows how to build a network suitable for solving the questions posed to it
  • aids in choosing or designing the best random graph model for comparing analytical results
  • how to design the best model for describing a given graph

Network analysis provides a perspective on how to find and quantify significant structures in the interaction patterns between different types of actors and on how to relate these structures to properties of the actors. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different types of random graph models, centrality indices, clustering algorithms, global network measures, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy – the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing an extensive glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Additionally, it provides and explains in detail R code for all analyses and diagrams shown in the book. Different aspects of network analysis literacy – understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation – are deepened in various exercises with provided solutions. This text is the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.

Content Level » Research

Keywords » identifying central nodes in social networks - network analysis design

Related subjects » Complexity - Information Systems and Applications

Popular Content within this publication 



Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Computer Appl. in Social and Behavioral Sciences.