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Entropy and the Complexity of Graphs Revisited

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Department of Computer Science, The City College of New York (CUNY), 138th Street at ConventAvenue, New York, NY 10031, USA
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Institute for Bioinformatics and Translational Research, UMIT, Eduard Wallnoefer Zentrum 1, 6060, Hall in Tyrol, Austria
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Authors to whom correspondence should be addressed.
Entropy 2012, 14(3), 559-570; https://doi.org/10.3390/e14030559
Received: 16 January 2012 / Revised: 5 March 2012 / Accepted: 12 March 2012 / Published: 14 March 2012
This paper presents a taxonomy and overview of approaches to the measurement of graph and network complexity. The taxonomy distinguishes between deterministic (e.g., Kolmogorov complexity) and probabilistic approaches with a view to placing entropy-based probabilistic measurement in context. Entropy-based measurement is the main focus of the paper. Relationships between the different entropy functions used to measure complexity are examined; and intrinsic (e.g., classical measures) and extrinsic (e.g., Körner entropy) variants of entropy-based models are discussed in some detail. View Full-Text
Keywords: complex networks; Shannon entropy; graph entropy complex networks; Shannon entropy; graph entropy
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MDPI and ACS Style

Mowshowitz, A.; Dehmer, M. Entropy and the Complexity of Graphs Revisited. Entropy 2012, 14, 559-570. https://doi.org/10.3390/e14030559

AMA Style

Mowshowitz A, Dehmer M. Entropy and the Complexity of Graphs Revisited. Entropy. 2012; 14(3):559-570. https://doi.org/10.3390/e14030559

Chicago/Turabian Style

Mowshowitz, Abbe, and Matthias Dehmer. 2012. "Entropy and the Complexity of Graphs Revisited" Entropy 14, no. 3: 559-570. https://doi.org/10.3390/e14030559

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