Fitness model

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For the human model with the physical fitness emphasis, see Model (person)#Fitness models.

In complex network theory, fitness is an inherent competitive factor that nodes may have, capable of affecting the network's evolution.[1] According to this idea, the nodes' intrinsic ability to attract links in the network varies from node to node, the most efficient (or "fit") being able to gather more edges in the expense of others. In that sense, not all nodes are identical to each other, and they claim their degree increase according to the fitness they possess every time. The fitness factors of all the nodes composing the network may form a distribution ρ(η) characteristic of the system been studied.

Bianconi and Barabási[2] proposed a variant to the Barabási-Albert model (BA model), where the probability for a node to connect to another one is supplied with a term expressing the fitness of the node involved. The fitness parameter is time independent and is multiplicative to the probability

\Pi_{i} = \frac{\eta_{i}k_{i}}{\sum_{j}\eta_{j}k_{j}}.

Thus, the system of equations for the time evolution of the degrees ki according to the continuum theory introduced by the same model will have the form

\frac{\partial k_{i}}{\partial t} = m\Pi_{i} = m\frac{\eta_{i}k_{i}}{\sum_{j}\eta_{j}k_{j}}

where m the number of edges the newly coming node has. If we require the solution to have a similar form to the one it had without the insertion of the fitness factors (to avoid ruining the power-law degree distribution of scale-free networks), then the exponent of the solution has to change and become fitness dependent

k_{\eta}(t,t_{i}) = m\left(\frac{t}{t_i}\right)^{\beta(\eta_{i})}

where

\beta(\eta) = \frac{\eta}{C}\mbox{ and }C = \int\rho(\eta)\frac{\eta}{1-\beta(\eta)}\,d\eta.

Hence, the more fit nodes increase their degree faster than the less ones. This characteristic attributes the network with a different behavior regarding it's evolution. Without the introduction of the fitness property, all nodes had the same exponent in the power-law degree evolution formula. This means that the older nodes in the system would have more edges compared to newcoming ones. After the fitness property is introduced, this exponent, and accordingly, the slope of k(t) change, giving thus the opportunity to newcoming nodes to dominate the system.

It was seen through this example how can a network's evolution change behavior through the introduction of a new parameter in the model. However, we require the network to preserve it's overall scale-free character. By forcing the fitness dependence to be accumulated in the exponent only, the degree-distribution will still be a power-law relationship, composed though by a weighted sum of different power-law in degree-evolution formulas

P(k) \sim \int\rho(\eta)\frac{C}{\eta}(m/k)^{C/\eta +1}

where ρ(η) is the fitness distribution depending on the system's composition

The fitness model can be extended to in corporate additional processes, such as internal edges, which affect the exponents[3].

[edit] See also

[edit] References

  1. ^ Albert R. and Barabási A.-L., "Statistical mechanics of complex networks", Reviews of Modern Physics 74, 47 (2002)
  2. ^ Bianconi G. and Barabási A.-L., 2001a, Europhys. Lett. 54, 436.
  3. ^ Ergun, G. and G. J. Rodgers, Physica A 303 (2002) 261-272