Authors:
- Teaches how to pose a defined network analytic question
- Demonstrates how to design the best model for describing a given graph
- Aids in choosing or designing the best random graph model for comparing analytical results
- Helps to interpret network analytic results
- Discusses ethical questions regarding network analysis
- Contains exercises and programming problems
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Social Networks (LNSN)
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Table of contents (15 chapters)
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Front Matter
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Introduction
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Front Matter
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Back Matter
About this book
This book presents a perspective of network analysis as a tool to find and quantify significant structures in the interaction patterns between different types of entities. Moreover, network analysis provides the basic means to relate these structures to properties of the entities. 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 global network measures, types of random graph models, centrality indices, 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 a glossary and intensive translation of formal notation and mathematical symbols in everyday speech. 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 an excellent, if not the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.
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Authors and Affiliations
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Graph Theory and Analysis of Complex Networks, TU Kaiserslautern, FB Computer Science, Graph Theory and Analysis of Complex Net, Heidelberg, Germany
Katharina A. Zweig
About the author
Bibliographic Information
Book Title: Network Analysis Literacy
Book Subtitle: A Practical Approach to the Analysis of Networks
Authors: Katharina A. Zweig
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-7091-0741-6
Publisher: Springer Vienna
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag GmbH Austria 2016
Hardcover ISBN: 978-3-7091-0740-9Published: 27 October 2016
Softcover ISBN: 978-3-7091-4877-8Published: 22 April 2018
eBook ISBN: 978-3-7091-0741-6Published: 26 October 2016
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: XXIII, 535
Number of Illustrations: 112 b/w illustrations, 14 illustrations in colour
Topics: Computer Appl. in Social and Behavioral Sciences, Applications of Graph Theory and Complex Networks, Complexity, Data-driven Science, Modeling and Theory Building, Data Mining and Knowledge Discovery