Alexandre Arenas

TL;DR: In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications.

TL;DR: This work introduces a method based on quantum theory to reduce the number of layers to a minimum while maximizing the distinguishability between the multilayer network and the corresponding aggregated graph.

TL;DR: The tensorial formalism recently proposed to characterize and investigate this kind of complex topologies is relied on, and two well known random walk centrality measures, the random walk betweenness and closeness centrality are extended to interconnected multilayer networks.

TL;DR: This paper analyzes multiple datasets each one consisting of individuals' online activity before, during and after an exceptional event in terms of volume of the communications registered, suggesting that models of online activity cannot discard the information carried by this multilayer representation of the system, and should account for the different processes generated by the different kinds of interactions.

TL;DR: A self-consistent correction to the Kolmogorov exponent is computed and it is found that for the model it is zero and the higher-order structure functions diverge for orders larger than a certain threshold, as theorized in some recent work.