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Towards an Information Theory of Complex Networks

Overview of attention for book
Cover of 'Towards an Information Theory of Complex Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Entropy of Digraphs and Infinite Networks
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    Chapter 2 An Information-Theoretic Upper Bound on Planar Graphs Using Well-Orderly Maps
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    Chapter 3 Towards an information theory of complex networks
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    Chapter 4 Wave Localization on Complex Networks
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    Chapter 5 Information-Theoretic Methods in Chemical Graph Theory
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    Chapter 6 On the Development and Application of Net-Sign Graph Theory
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    Chapter 7 The Central Role of Information Theory in Ecology
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    Chapter 8 Inferences About Coupling from Ecological Surveillance Monitoring: Approaches Based on Nonlinear Dynamics and Information Theory
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    Chapter 9 Markov Entropy Centrality: Chemical, Biological, Crime, and Legislative Networks
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    Chapter 10 Social Ontologies as Generalized Nearly Acyclic Directed Graphs: A Quantitative Graph Model of Social Tagging
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    Chapter 11 Typology by Means of Language Networks: Applying Information Theoretic Measures to Morphological Derivation Networks
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    Chapter 12 Information Theory-Based Measurement of Software
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    Chapter 13 Fair and Biased Random Walks on Undirected Graphs and Related Entropies
Attention for Chapter 12: Information Theory-Based Measurement of Software
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Citations

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Chapter title
Information Theory-Based Measurement of Software
Chapter number 12
Book title
Towards an Information Theory of Complex Networks
Published by
Birkhäuser Boston, January 2011
DOI 10.1007/978-0-8176-4904-3_12
Book ISBNs
978-0-8176-4903-6, 978-0-8176-4904-3
Authors

Edward B. Allen, Allen, Edward B.

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 33%
Unknown 2 67%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 67%
Student > Doctoral Student 1 33%
Readers by discipline Count As %
Business, Management and Accounting 1 33%
Computer Science 1 33%
Unknown 1 33%

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