On the Interplay of Network Structure and Routing Strategies on Network Design Methods for Mitigation of Intentional Attacks in Scale-Free Networks

2016 ◽  
Vol 25 (3) ◽  
pp. 508-535 ◽  
Author(s):  
Walid K. Ghamry ◽  
Suzan Shukry
Keyword(s):  
Network Design ◽  
Network Structure ◽  
Design Methods ◽  
Scale Free ◽  
Scale Free Networks

scholarly journals OPTIMIZATION OF ROBUSTNESS OF SCALE-FREE NETWORK TO RANDOM AND TARGETED ATTACKS

2005 ◽  
Vol 19 (16) ◽  
pp. 785-792 ◽  
Author(s):  
JIAN-GUO LIU ◽  
ZHONG-TUO WANG ◽  
YAN-ZHONG DANG
Keyword(s):  
Network Design ◽  
Scale Free Network ◽  
Design Guideline ◽  
Scale Free ◽  
Targeted Attacks ◽  
Maximum Value ◽  
Scale Free Networks ◽  
Free Network ◽  
Random Failures ◽  
Connectivity Distribution

Scale-free networks, having connectivity distribution P(k)~k-α (where k is the site connectivity), are very resilient to random failures but are fragile to intentional attacks. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attacks maximum while keeping the average connectivity <k> per node constant. We find that when <k> = 3 the robustness of the scale-free networks reach its maximum value if the minimal connectivity m = 1, but when <k> is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity m gets larger.

EFFECTS OF CONFIDENCE SCALE AND NETWORK STRUCTURE ON MINORITY OPINION SPREADING

2010 ◽  
Vol 21 (08) ◽  
pp. 1001-1010 ◽  
Author(s):  
BO SHEN ◽  
YUN LIU
Keyword(s):  
Simple Model ◽  
Network Structure ◽  
Random Network ◽  
Random Networks ◽  
Scale Free Network ◽  
Opinion Formation ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Confidence Scale

We study the dynamics of minority opinion spreading using a proposed simple model, in which the exchange of views between agents is determined by a quantity named confidence scale. To understand what will promote the success of minority, two types of networks, random network and scale-free network are considered in opinion formation. We demonstrate that the heterogeneity of networks is advantageous to the minority and exchanging views between more agents will reduce the opportunity of minority's success. Further, enlarging the degree that agents trust each other, i.e. confidence scale, can increase the probability that opinions of the minority could be accepted by the majority. We also show that the minority in scale-free networks are more sensitive to the change of confidence scale than that in random networks.

THE GINI COEFFICIENT'S APPLICATION TO GENERAL COMPLEX NETWORKS

2005 ◽  
Vol 08 (01) ◽  
pp. 159-167 ◽  
Author(s):  
HAI-BO HU ◽  
LIN WANG
Keyword(s):  
Income Inequality ◽  
Complex Networks ◽  
Network Structure ◽  
Gini Coefficient ◽  
Scale Free ◽  
The Gini Coefficient ◽  
Scale Free Networks ◽  
General Complex

The Gini coefficient, which was originally used in microeconomics to describe income inequality, is introduced into the research of general complex networks as a metric on the heterogeneity of network structure. Some parameters such as degree exponent and degree-rank exponent were already defined in the case of scale-free networks also as a metric on the heterogeneity. In scale-free networks, the Gini coefficient is proved to be equivalent to the parameters mentioned above, and moreover, a classification of infinite scale-free networks is given according to the value of the Gini coefficient.

scholarly journals The evolution of trust and trustworthiness

2020 ◽  
Vol 17 (169) ◽  
pp. 20200491 ◽  
Author(s):  
Aanjaneya Kumar ◽  
Valerio Capraro ◽  
Matjaž Perc
Keyword(s):  
Network Structure ◽  
Trust Game ◽  
Scale Free Network ◽  
Scale Free ◽  
Scale Free Networks ◽  
Free Network ◽  
Moral Behaviour ◽  
Mixed Populations ◽  
Parameter Values ◽  
Future Work

Trust and trustworthiness form the basis for continued social and economic interactions, and they are also fundamental for cooperation, fairness, honesty, and indeed for many other forms of prosocial and moral behaviour. However, trust entails risks, and building a trustworthy reputation requires effort. So how did trust and trustworthiness evolve, and under which conditions do they thrive? To find answers, we operationalize trust and trustworthiness using the trust game with the trustor’s investment and the trustee’s return of the investment as the two key parameters. We study this game on different networks, including the complete network, random and scale-free networks, and in the well-mixed limit. We show that in all but one case, the network structure has little effect on the evolution of trust and trustworthiness. Specifically, for well-mixed populations, lattices, random and scale-free networks, we find that trust never evolves, while trustworthiness evolves with some probability depending on the game parameters and the updating dynamics. Only for the scale-free network with degree non-normalized dynamics, we find parameter values for which trust evolves but trustworthiness does not, as well as values for which both trust and trustworthiness evolve. We conclude with a discussion about mechanisms that could lead to the evolution of trust and outline directions for future work.

scholarly journals Scale-Free Exponents of Resting State are Biomarkers of Neuro-Typical and Atypical Brain Activity

2016 ◽  
Author(s):  
S.J. Hanson ◽  
D. Mastrovito ◽  
C. Hanson ◽  
J. Ramsey ◽  
C. Glymour
Keyword(s):  
Network Structure ◽  
Resting State ◽  
Fault Tolerant ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Network ◽  
Single Individual ◽  
Scale Free ◽  
Scale Free Networks ◽  
The Brain

AbstractScale-free networks (SFN) arise from simple growth processes, which can encourage efficient, centralized and fault tolerant communication (1). Recently its been shown that stable network hub structure is governed by a phase transition at exponents (>2.0) causing a dramatic change in network structure including a loss of global connectivity, an increasing minimum dominating node set, and a shift towards increasing connectivity growth compared to node growth. Is this SFN shift identifiable in atypical brain activity? The Pareto Distribution (P(D)∼D∧-β) on the hub Degree (D) is a signature of scale-free networks. During resting-state, we assess Degree exponents across a large range of neurotypical and atypical subjects. We use graph complexity theory to provide a predictive theory of the brain network structure. Results.We show that neurotypical resting-state fMRI brain activity possess scale-free Pareto exponents (1.8 se .01) in a single individual scanned over 66 days as well as in 60 different individuals (1.8 se .02). We also show that 60 individuals with Autistic Spectrum Disorder, and 60 individuals with Schizophrenia have significantly higher (>2.0) scale-free exponents (2.4 se .03, 2.3 se .04), indicating more fractionated and less controllable dynamics in the brain networks revealed in resting state. Finally we show that the exponent values vary with phenotypic measures of atypical disease severity indicating that the global topology of the network itself can provide specific diagnostic biomarkers for atypical brain activity.

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