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First, we generalize degree diversity into multiplex networks to improve the prospect for cooperation.
We use, in particular, multiplex networks to provide the comprehensive conceptual framework, see e.g., [1, 18, 30, 36 43], and random walks to study the mobility of commuters within a multimodal transportation network in a city.
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The systems will also be subjected to community detection algorithms both separately and as a multiplex network to compare their similarities, differences, and interactions.
Multiplex networks are closer to reality as they encapsulate various heterogeneous relations rather than just one.
The idea is to build different multiplex networks of two layers to mimic the two transportation modes under study.
Li et al.[33] utilized attractor selection in wavelength division multiplexing mesh networks to execute intentional path reroutes especially for unpredictable future resource demands.
Thus, these algorithms give mixed results on multiplex networks and are unable to use the information from different layers in an efficient manner.
Our experiments revealed different convergence patterns of the coverage in multiplex networks that are related to the topological characteristics of their underlying graphs.
Regarding point (i) above, our work adds to the literature on multiplex networks, which has gained a lot of momentum in the last five years.
In this work, we propose an approach that can be utilized to predict links in multiplex networks where the extension of pre-existing algorithms is not viable.
Previous approaches proposed a mathematical framework to numerically compute the coverage in multiplex networks.
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