Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks
- PDF / 8,074,359 Bytes
- 34 Pages / 595.276 x 790.866 pts Page_size
- 66 Downloads / 227 Views
plied Network Science
Open Access
RESEARCH
Layer entanglement in multiplex, temporal multiplex, and coupled multilayer networks Blaž Škrlj1* and Benjamin Renoust2 *Correspondence: [email protected] 1 Jožef Stefan International Postgraduate School and Jožef Stefan Institute, Ljubljana, Slovenia Full list of author information is available at the end of the article
Abstract 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. Keywords: Multiplex networks, Layer entanglement, Temporal network, Network topology, Network generator
Introduction A real world complex system often counts multiple interactions between multiple different entities. When these interactions are regrouped under multiple families of entities, multilayer network modelling becomes a tool of choice to capture the key components of the system. The use of this model emerges in all fields of science from social sciences to finances, logistics, biology, and many more (Kivelä et al. 2014). With multilayer networks, the study of multiple viewpoints [or aspects (Kivelä et al. 2019)] on the same network data becomes possible. This is critical for example in social network analysis, to study the role of users in different networks, and compare them (for example the same individual may behave differently on LinkedIn, Twitter, or Facebook). These different networks form different types of links that may be overlaid.
© The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Crea
Data Loading...
