  Complex networks, such as transportation networks, social networks, or
biological networks, capture the complex system they model often by
representing only one type of interactions. In real world systems, there may be
many different aspects that connect entities together. These can be captured
using multilayer networks, which combine different modalities of interactions
in a single model. Coupling in multilayer networks may exhibit different
properties which can be related to the very nature of the data they model (or
to events in time-dependant data). We hypothesise that such properties may be
reflected in the way layers are intertwined. In this paper, we investigated
these through the prism of layer entanglement in coupled multilayer networks.
We test over 30 real-life networks in 6 different disciplines (social, genetic,
transport, co-authorship, trade, and neuronal networks). We further propose a
random generator, displaying comparable patterns of elementary layer
entanglement and transition coupling entanglement across 1,329,696 synthetic
coupled multilayer networks. Our experiments demonstrate difference of layer
entanglement across disciplines, and even suggest a link between entanglement
intensity and homophily. We additionally study entanglement in 3 real world
temporal datasets displaying a potential rise in entanglement activity prior to
other network activity.
