Sentence examples for most network models from inspiring English sources

In most network models, the role of these inter-fibre bonds is not explicitly incorporated.

Even though in most network models and empirical networks various centrality measures are usually positively correlated, some types of regularities can cause them to be less so, our unit network did not show any such effects.

We stress that the non-linear statistical association highlighted by our results is not a natural feature of most network models.

To infer networks of contextual gene sets, each contextual gene set needs to be represented as a single variable as most network models assume each node as a single random variable.

Like in most network models with random connectivity, randomness here represents the simplest assumption (Amit 1989) and substitutes fine-scale structure addressed in other models (e.g. Lundqvist et al. 2010).

Historically, most network elasticity models have been based on idealized assumptions of uniform chain length constrained to highly symmetric orientations.

And the RBNN is one of the most used network models.

For example, as one of the most acknowledged network models, the preferential attachment model uses the CND of a node to determine the probability of whether to add a new edge to that node (Barabási and Albert 1999).

Most biochemical network models published to date have been based on mass action kinetic-based ordinary differential equations.

However, the most advanced network models will fail to reveal the underlying biology unless they closely resemble the structures that determine the attributes of their functional networks 101.

Considering the importance of robustness of biological systems over timing variations, timing robustness analysis needs to be carefully considered in most Boolean network models.