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| 然而对于<math>m=1</math>,它表现了赢者通吃的机制,因为可以发现几乎<math>99\%</math> 的节点的度仅为1,而某一个节点的度超级大。当<math>m</math>的值增加时,超级富人和穷人之间的差距减小;当<math>m>14</math>时,我们发现了从赢者通吃机制到富人更富机制的转变。 | | 然而对于<math>m=1</math>,它表现了赢者通吃的机制,因为可以发现几乎<math>99\%</math> 的节点的度仅为1,而某一个节点的度超级大。当<math>m</math>的值增加时,超级富人和穷人之间的差距减小;当<math>m>14</math>时,我们发现了从赢者通吃机制到富人更富机制的转变。 |
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− | === Fitness model ===
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− | Another model where the key ingredient is the nature of the vertex has been introduced by Caldarelli et al.<ref>Caldarelli G., A. Capocci, P. De Los Rios, M.A. Muñoz, Physical Review Letters 89, 258702 (2002)</ref> Here a link is created between two vertices <math>i,j</math> with a probability given by a linking function <math>f(\eta_i,\eta_j)</math> of the [[Fitness model (network theory)|fitnesses]] of the vertices involved.
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− | The degree of a vertex i is given by <ref>Servedio V.D.P., G. Caldarelli, P. Buttà, Physical Review E 70, 056126 (2004)</ref>
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− | :<math>k(\eta_i)=N\int_0^\infty f(\eta_i,\eta_j) \rho(\eta_j) \, d\eta_j </math>
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− | If <math>k(\eta_i)</math> is an invertible and increasing function of <math>\eta_i</math>, then
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− | the probability distribution <math>P(k)</math> is given by
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− | :<math>P(k)=\rho(\eta(k)) \cdot \eta'(k)</math>
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− | As a result, if the fitnesses <math>\eta</math> are distributed as a power law, then also the node degree does.
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− | Less intuitively with a fast decaying probability distribution as
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− | <math>\rho(\eta)=e^{-\eta}</math> together with a linking function of the kind
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− | :<math> f(\eta_i,\eta_j)=\Theta(\eta_i+\eta_j-Z)</math>
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− | with <math>Z</math> a constant and <math>\Theta</math> the Heavyside function, we also obtain
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− | scale-free networks.
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− | Such model has been successfully applied to describe trade between nations by using GDP as fitness for the various nodes <math>i,j</math> and a linking function of the kind
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− | <ref>Garlaschelli D., M I Loffredo Physical Review Letters 93, 188701 (2004)</ref>
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− | <ref>Cimini G., T. Squartini, D. Garlaschelli and A. Gabrielli, Scientific Reports 5, 15758 (2015)</ref>
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− | :<math> \frac{\delta \eta_i\eta_j}{1+ \delta \eta_i\eta_j}.</math>
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| === 适应度模型 === | | === 适应度模型 === |