Sentence examples for exponential networks from inspiring English sources

In exponential networks, the vertex degree distribution is described by the relationship P(k) ∼ e−γk.

In exponential networks the probability that a node has a high number of connections is very low.

This consistently returned randomly scaled (exponential) networks because the 0.17 intercept term dominates the probability of attachment.

They differ from exponential networks in having a broad or 'fat' tail – that is, the number of nodes with a large number of connections is more than expected under a random attachment model.

In particular, random (symmetric) "subfunctionalization" between protein duplicates at the level of protein domains does not prevent the emergence of scale-free networks with locally conserved topology, by contrast to random link "complementation" at the level of individual interactions (Fig. S1) which leads to exponential networks without conserved topology (see Supporting Information).

The Exponential Network.

The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays.

The slope of the regression line provides an estimate for the scaling factor γ. Regression analysis for the exponential network model was carried out similarly.

Thus, the scale-free model explained the data much better than the exponential model and we concluded that the PubLiME co-occurrence network represented more likely a scale-free network than an exponential network.

This demonstrates, from first principles, that evolutionary conservation and scale-free topology are intrinsically linked properties of PPI networks and emerge from i) prevailing exponential network dynamics under duplication and ii) asymmetric divergence of gene duplicates.

Regression analysis was applied to estimate the scaling factors for the scale-free and exponential network models, as well as to investigate the relationship between the clustering coefficient C(k) and the vertex degree k.