We study the dynamics of diffusion processes acting on directed multiplex networks, i.e., coupled multilayer networks where at least one layer consists of a directed graph. By numerical and analytical analysis, we show a new phenomenology, which is genuinely induced by the directionality of the links: the emergence of a prime regime of coupling for which directed multiplex networks exhibit a faster diffusion at an intermediate degree of coupling than when the two layers are fully coupled.
Foundations of Complex Systems
Multiplexity and temporal evolution are both known to be structural features that critically affect dynamical behaviors on complex networks. By generalizing the theory of Masuda et al. to multiplex networks, we investigate how the combination of such features impacts on a diffusive dynamics. As in the case of single layer networks, diffusive dynamics is slower in a temporal multiplex network than in its temporal-aggregate version. However, because of a non-linear rescaling of the inter-layer diffusion constant appearing in the solution, we observe a richer phenomenology.
Disentangling network macroscopic structures is one of the funding problems in complexity science. One of the most basic models of communities in networks are the stochastic block models. It was recently shown that in this case the detectability of real communities only from the network topology is limited. Even though the results were shown only for planted partition, where there are only two parameters, the conclusions are universal.
There is a burst of work on human mobility and encounter networks. However, the connection between these two important fields just begun recently. It is clear that both are closely related: Mobility generates encounters, and these encounters might give rise to contagion phenomena or even friendship. We model a set of random walkers that visit locations in space following a strategy akin to Lévy flights. We measure the encounters in space and time and establish a link between walkers after they coincide several times.
Online food delivery services rely on urban transportation to alleviate customers’ burden of traveling in highly dense cities. As new business models, these services exploit user-generated contents to promote collaborative consumption among its members. This study aims to evaluate the impact of traffic conditions (through the use of Google Maps API) on key performance indicators of online food delivery services (through the use of web scraping techniques to retrieve customer’s ratings and the physical location of restaurants as provided by Facebook).