Complex networks, such as transportation networks, social networks, or biological networks, capture the complex system they model by often 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-dependent 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.

诸如运输网络，社交网络或生物网络之类的复杂网络通常仅代表一种类型的交互，从而捕获了它们所建模的复杂系统。在现实世界的系统中，可能会有许多不同的方面将实体连接在一起。可以使用多层网络捕获这些数据，这些网络在单个模型中结合了不同的交互方式。多层网络中的耦合可能表现出不同的属性，这可能与它们建模的数据的本质（或与时间相关的数据中的事件）有关。我们假设这样的属性可能会在层间缠绕的方式中得到反映。在本文中，我们通过耦合多层网络中层缠结的棱镜研究了它们。我们测试了6个不同学科（社会，遗传，交通，合作，交易和神经网络）。我们进一步提出了一个随机生成器，该生成器显示了1,329,696个合成耦合多层网络中基本层缠结和过渡耦合缠结的可比模式。我们的实验证明了各学科之间层纠缠的不同，甚至暗示了纠缠强度和同构性之间的联系。我们还研究了3个现实世界时态数据集中的纠缠，显示出纠缠活动在其他网络活动之前的潜在上升。甚至暗示了纠缠强度和同构性之间的联系。我们还研究了3个现实世界时态数据集中的纠缠，显示出纠缠活动在其他网络活动之前的潜在上升。甚至暗示了纠缠强度和同构性之间的联系。我们还研究了3个现实世界时态数据集中的纠缠，显示出纠缠活动在其他网络活动之前的潜在上升。