Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


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Editorial Advisory Board's Chair
William Lesso

Editor-in-Chief
Nagib C. Callaos


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The International Institute of
Informatics and Systemics

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No Warranty Express or Implied: Why Do We Have So Many Problems With the Computer Systems That Pervade Our Lives?
John W. Coffey
(pages: 1-6)

Can You Hear Me Now? An Innovative Approach to Assess and Build Connections with Online Learner’s
Tina M. Serafini, Risa Blair
(pages: 7-11)

End-to-end Security with Translation
Kevin E. Foltz
(pages: 12-17)

(Assistive) Technology at the Point of Instruction: Barriers and Possibilities
Lorayne Robertson
(pages: 18-24)

Supplementing Multiple Modalities and Universal Design in Learning with Goal-Setting
Russell Jay Hendel
(pages: 25-30)

Experts Informing Experts
Robert Hammond
(pages: 31-35)

Internet of Things – A New Epistemic Object
Rolf Dornberger, Terry Inglese, Safak Korkut
(pages: 36-44)

An Experiment in Interdisciplinary STEM Education: Insights from the Catholic Intellectual Tradition
Fr. Joseph R. Laracy, Thomas Marlowe, Fr. Gerald J. Buonopane
(pages: 45-53)

Big History Understanding of Complexity, Informatics and Cybernetics
John L. Motloch
(pages: 54-60)

Flourishing Organizations
Maria Jakubik
(pages: 61-72)

Pros & Cons of Smart ICT in Some Governmental Applications
Dusan Soltes
(pages: 73-75)

Information Exchange in Vehicles Ad-Hock Networks
Tomas Zelinka
(pages: 76-80)

Living in a Digital World: Improving Skills to Meet the Challenges of Digital Transformation Through Authentic and Game-Based Learning
Margit Scholl, Frauke Fuhrmann
(pages: 81-86)

Psychotherapy via the Internet as a Novel Tool for Clinical Use
Ulrich Sprick
(pages: 87-94)

Technology Intercepts for Cyber Security Applied to Critical Infrastructures
Mario La Manna
(pages: 95-100)

“And Then a Miracle Occurs …” – Engaging the Challenge of Operationalizing Theories of Success in Digital Transformation
Michael Von Kutzschenbach
(pages: 101-105)

Multidisciplinary Learning Extends Communication Skill, and Helps Cross Cultural Understandings: Biomedical Engineering
Shigehiro Hashimoto
(pages: 106-112)

Integrating Teaching, Research and Problem Solving: An Experience in Progress in the Mucuri Valley Region (Brazil)
Leônidas Conceição Barroso
(pages: 113-118)

Meeting Learning Challenges in Product Design Education with and through Additive Manufacturing
William Lavatelli Kempton, Steinar Killi, Andrew Morrison
(pages: 119-129)

Creating and Using Symbolic Mental Structures via Piaget’s Constructivism and Popper’s Three Worlds View with Falsifiability to Achieve Critical Thinking by Students in the Physical Sciences
Matthew E. Edwards
(pages: 130-134)

Creativity in Higher Education: Comparative Genetic Analyses on the Dopaminergic System in Relation to Creativity, Addiction, Schizophrenia in Humans and Non-Human Primates
Bernard Wallner, Sonja Windhager, Katrin Schaefer, Martin Fieder
(pages: 135-142)


 

Abstracts

 


ABSTRACT


Network Complexity Measures. An Information-Theoretic Approach.

Matthias Dehmer, Stefan Pickl


Quantitative graph analysis by using structural indices has been intricate in a sense that it often remains unclear which structural graph measures is the most suitable one, see [1, 12, 13]. In general, quantitative graph analysis deals with quantifying structural information of networks by using a measurement approach [5]. As special problem thereof is to characterize a graph quantitatively, that means to determine a measure that captures structural features of a network meaningfully. Various classical structural graph measures have been used to tackle this problem [13]. A fruitful approach by using information-theoretic [21] and statistical methods is to quantify the structural information content of a graph [1, 8, 18].

In this note, we sketch some classical information measures. Also, we briefly address the problem what kind of measures capture structural information uniquely. This relates to determine the discrimination power (or also called uniqueness) of a graph measure, that is, how is the ability of the measures to discriminate non-isomorphic graphs structurally.

[1] D. Bonchev. Information Theoretic Indices for Characterization of Chemical Structures. Research Studies Press, Chichester, 1983.
[5] M. Dehmer and F. Emmert-Streib. Quantitative Graph Theory. Theory and Applications. CRC Press, 2014.
[8] M. Dehmer, M. Grabner, and K. Varmuza. Information indices with high discriminative power for graphs. PLoS ONE, 7:e31214, 2012.
[12] F. Emmert-Streib and M. Dehmer. Exploring statistical and population aspects of network complexity. PLoS ONE, 7:e34523, 2012.
[13] F. Harary. Graph Theory. Addison Wesley Publishing Company, 1969. Reading, MA, USA.
[18] A. Mowshowitz. Entropy and the complexity of the graphs I: An index of the relative complexity of a graph. Bull. Math. Biophys., 30:175–204, 1968.
[21] C. E. Shannon and W. Weaver. The Mathematical Theory of Communication. University of Illinois Press, 1949.

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