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Entropy and the Complexity of Graphs Revisited

Department of Computer Science, The City College of New York (CUNY), 138th Street at ConventAvenue, New York, NY 10031, USA
Institute for Bioinformatics and Translational Research, UMIT, Eduard Wallnoefer Zentrum 1, 6060, Hall in Tyrol, Austria
Authors to whom correspondence should be addressed.
Entropy 2012, 14(3), 559-570;
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.

AMA Style

Mowshowitz A, Dehmer M. Entropy and the Complexity of Graphs Revisited. Entropy. 2012; 14(3):559-570.

Chicago/Turabian Style

Mowshowitz, Abbe, and Matthias Dehmer. 2012. "Entropy and the Complexity of Graphs Revisited" Entropy 14, no. 3: 559-570.

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