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Our results show that there are differences in the way users communicate in the two channels that are reflected in the shape of degree- and interevent time distributions.
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On each small interval we use hermitian shape functions of degree 3 to approximate the unknown function which is the solution of the system under study.
With RRC transmit pulse shape filter of degree 64 and r = 4, the starting index which maximizes the sum-power is k = 2.
Hence, already after a short period, a sufficient amount of data has been collected to correctly estimate the stationary degree distribution of the network, and the shape of the degree remains similar for longer intervals.
The shape of the degree distribution curves in Fig. 8 also offers some interesting observations.
Characterizing the exact shape of the degree distribution is not an easy task, which has been the focus of a study by Seshradi et al. [23].
The model highlights the role of anatomical resolution as one of the key ingredients necessary to define the functional shape of the degree distributions.
The shape of a degree distribution, for example whether it follows an exponential or power-law form, is typically taken to be indicative of the processes structuring the network.
We want to focus on the effect of implementing protein families rather than other topological characteristics such as for example the shape of the degree distribution.
We have recently studied statistical sampling properties of network ensembles [ 4, 5] in considerable detail: the results suggest that when ≳80% of the nodes in a network are sampled at random, the shape of the degree distribution of the subnet, Pr* k), will be virtually indistinguishable from that of the true network.
The exponential shape of the node degree distribution seen here indicates that the level of connectivity falls off more rapidly than seen for the other gene coexpression networks that show power-law distributions.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com