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A detailed characterization of complex networks using Information Theory

journal contribution
posted on 28.05.2021, 20:25 by CGS Freitas, ALL Aquino, HS Ramos, Alejandro FreryAlejandro Frery, OA Rosso
Understanding the structure and the dynamics of networks is of paramount importance for many scientific fields that rely on network science. Complex network theory provides a variety of features that help in the evaluation of network behavior. However, such analysis can be confusing and misleading as there are many intrinsic properties for each network metric. Alternatively, Information Theory methods have gained the spotlight because of their ability to create a quantitative and robust characterization of such networks. In this work, we use two Information Theory quantifiers, namely Network Entropy and Network Fisher Information Measure, to analyzing those networks. Our approach detects non-trivial characteristics of complex networks such as the transition present in the Watts-Strogatz model from k-ring to random graphs; the phase transition from a disconnected to an almost surely connected network when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-free networks when considering a non-linear preferential attachment, fitness, and aging features alongside the configuration model with a pure power-law degree distribution. Finally, we analyze the numerical results for real networks, contrasting our findings with traditional complex network methods. In conclusion, we present an efficient method that ignites the debate on network characterization.

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Preferred citation

Freitas, C. G. S., Aquino, A. L. L., Ramos, H. S., Frery, A. C. & Rosso, O. A. (2019). A detailed characterization of complex networks using Information Theory. Scientific Reports, 9(1), 16689-. https://doi.org/10.1038/s41598-019-53167-5

Journal title

Scientific Reports

Volume

9

Issue

1

Publication date

01/12/2019

Pagination

16689

Publisher

Springer Science and Business Media LLC

Publication status

Published

Online publication date

13/11/2019

ISSN

2045-2322

eISSN

2045-2322

Article number

16689

Language

en

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