scholarly journals Author Correction: A detailed characterization of complex networks using Information Theory (Scientific Reports, (2019), 9, 1, (16689), 10.1038/s41598-019-53167-5)

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Detailed Characterization

© 2021, The Author(s). An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Author Correction: A detailed characterization of complex networks using Information Theory (Scientific Reports, (2019), 9, 1, (16689), 10.1038/s41598-019-53167-5)

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Detailed Characterization

© 2021, The Author(s). An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals Author Correction: A detailed characterization of complex networks using Information Theory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Cristopher G. S. Freitas ◽  
Andre L. L. Aquino ◽  
Heitor S. Ramos ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Detailed Characterization

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

scholarly journals A detailed characterization of complex networks using Information Theory

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

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.

scholarly journals A detailed characterization of complex networks using Information Theory

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Cristopher G. S. Freitas ◽  
Andre L. L. Aquino ◽  
Heitor S. Ramos ◽  
Alejandro C. Frery ◽  
Osvaldo A. Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

Abstract 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.

scholarly journals A detailed characterization of complex networks using Information Theory

2021 ◽  
Author(s):  
CGS Freitas ◽  
ALL Aquino ◽  
HS Ramos ◽  
Alejandro Frery ◽  
OA Rosso
Keyword(s):  
Information Theory ◽  
Complex Networks ◽  
Complex Network ◽  
Preferential Attachment ◽  
Configuration Model ◽  
Complex Network Theory ◽  
Scale Free ◽  
Network Entropy ◽  
Law Degree

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.

Intramolecular Proton Transfer in the Isomerization of Hydroxyacetone: A Detailed Characterization Based on Reaction Force Analysis and the Bond Fragility Spectrum

2020 ◽  
Author(s):  
Wallace Derricotte ◽  
Huiet Joseph
Keyword(s):  
Chemical Potential ◽  
Reaction Force ◽  
Intramolecular Proton Transfer ◽  
Intermediate Energy ◽  
Force Analysis ◽  
Activation Energy Barrier ◽  
Detailed Characterization ◽  
Proton Transfers ◽  
Reaction Force Constant

The mechanism of isomerization of hydroxyacetone to 2-hydroxypropanal is studied within the framework of reaction force analysis at the M06-2X/6-311++G(d,p) level of theory. Three unique pathways are considered: (i) a step-wise mechanism that proceeds through formation of the Z-isomer of their shared enediol intermediate, (ii) a step-wise mechanism that forms the E-isomer of the enediol, and (iii) a concerted pathway that bypasses the enediol intermediate. Energy calculations show that the concerted pathway has the lowest activation energy barrier at 45.7 kcal mol<sup>-1</sup>. The reaction force, chemical potential, and reaction electronic flux are calculated for each reaction to characterize electronic changes throughout the mechanism. The reaction force constant is calculated in order to investigate the synchronous/asynchronous nature of the concerted intramolecular proton transfers involved. Additional characterization of synchronicity is provided by calculating the bond fragility spectrum for each mechanism.

DETAILED CHARACTERIZATION OF EUROPA'S RIDGE MORPHOLOGY

2016 ◽  
Author(s):  
Janelle A.F. Heitmeier ◽  
◽  
Emily S. Martin ◽  
Jordan M. Bretzfelder ◽  
D. Alex Patthoff ◽  
...  
Keyword(s):  
Detailed Characterization

The Problem of Divine Hiddenness

2018 ◽  
Author(s):  
Michael C. Rea
Keyword(s):  
Problem Of Evil ◽  
Philosophical Discussion ◽  
Detailed Characterization ◽  
The Past ◽  
Divine Hiddenness ◽  
The Problem Of Evil ◽  
The Relationship

This chapter provides a detailed characterization of the various meanings of the term “divine hiddenness,” carefully and rigorously articulates the version of the problem of divine hiddenness that has dominated contemporary philosophical discussion for the past twenty-five years, and then explains the relationship between that problem and the problem of evil.

Acid-Sensing Ion Channels

2020 ◽  
Author(s):  
Stefan Gründer
Keyword(s):  
Nervous System ◽  
Ion Channels ◽  
Excitatory Synapses ◽  
Detailed Characterization ◽  
High Affinity ◽  
Acid Sensing Ion Channels ◽  
Long Time ◽  
Pathological Pain

Acid-sensing ion channels (ASICs) are proton-gated Na+ channels. Being almost ubiquitously present in neurons of the vertebrate nervous system, their precise function remained obscure for a long time. Various animal toxins that bind to ASICs with high affinity and specificity have been tremendously helpful in uncovering the role of ASICs. We now know that they contribute to synaptic transmission at excitatory synapses as well as to sensing metabolic acidosis and nociception. Moreover, detailed characterization of mouse models uncovered an unanticipated role of ASICs in disorders of the nervous system like stroke, multiple sclerosis, and pathological pain. This review provides an overview on the expression, structure, and pharmacology of ASICs plus a summary of what is known and what is still unknown about their physiological functions and their roles in diseases.

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