Abstract
Links in many real-world networks activate and deactivate in correspondence to the sporadic interactions between the elements of the system. The activation patterns may be irregular or bursty and play an important role on the dynamics of processes taking place in the network. Information or disease spreading in networks are paradigmatic examples of this situation. Besides burstiness, several correlations may appear in the process of link activation: memory effects imply temporal correlations, but also the existence of communities in the network may mediate the activation patterns of internal an external links. Here we study the competition of topological and temporal correlations in link activation and how they affect the dynamics of systems running on the network. Interestingly, both types of correlations by separate have opposite effects: one (topological) delays the dynamics of processes on the network, while the other (temporal) accelerates it. When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way.
Highlights
It is extensively reported that single Poisson processes, characterized by an exponential inter-event time distribution, do not faithfully describe real interactions patterns in a variety of systems
This section reports the impact of each type of correlations alone on the model dynamics
We compare the time to arrive at the absorbing state, Tabs, when correlated link activation is considered against the corresponding uncorrelated case
Summary
It is extensively reported that single Poisson processes, characterized by an exponential inter-event time distribution, do not faithfully describe real interactions patterns in a variety of systems. It has been reported that non-Poissonian inter-event time distributions, as well as temporal and topological correlations, contribute to slow down the dynamical processes on networks, especially when the dynamics on empirical networks is compared with that on randomized null models[15,20,23,29,30,31,37]. Some recent analytical results point to the existence of regimes where an acceleration of the dynamical process with respect to these null models might be possible[38,39,40] It has been pointed out the importance of different contributions to the evolution of both the network and the processes on top of it[41]. When both types are combined the final dynamics crucially depends on how the combination is built reflecting the competition between correlations
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