Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks
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Complex networks, such as transportation networks, social networks, or biological networks, translate the complex system they model often by representing one type of interactions. In real world systems, there may be many aspects of the entities that connects them together. These can be captured using multilayer networks, which combine different modalities of interactions in one same model. Coupling in multilayer networks and multiplex 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 on the way layers are intertwined. In this paper, we want to investigate these through the prism of layer entanglement in multilayer networks. We test them on 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 layer 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.
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