To show the deep property of embedded "deep" labels, a multilayer network architecture of our model is illustrated.
As a result, more sophisticated VHO algorithm is required to extend the throughput of multilayer network and to increase efficiency of resource management for next generation of HWNs.
Simulation shows that assortative network in percolation-cascading process performs better and when the layers of multilayer network meet with BA network the robustness of the network increases.
The results we obtained with this particular type to series, and also with some others like Henon and Logistic maps, clearly indicate that there is an interplay between the architecture of a multilayer network and the embedding dimension m of the time series used.
The participation process for UFT policy-making is described in the ABM by means of a multilayer network, where each layer represents a different level of description and details of the process.
We thus begin by exploring the correlations between layers in a multilayer network having three types of links, one based on interaction frequency, another based on collocation frequency, while the third is based on cognitive salience.
We can similarly define the core neighbourhood (denoted by CN) of a node i across layers of the multilayer network as: Gamma_{CNi} = bigl{ j in V^{mathcal{M}} : e_{i,j} in E^{alphacapbeta}bigr}, (2) where we define the set of multiplex links as (E^{alphacapbeta}).
Figure 7 (Left) A toy example showing the fundamental structure of the multilayer network we analyze.
The MCGC of a multilayer network is the largest component that remains after the random failure propagates back and forth in the different layers.
