Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9004
Title: Hidden Geometric correlations in real multiplex networks: multidimensional communities, trans-layer link prediction, and efficient navigation
Authors: Kleineberg, Kaj-Kolja 
Boguna, Marian 
Serrano, M. Angeles 
Papadopoulos, Fragkiskos 
Keywords: Physics - Physics and Society
Physics - Data Analysis
Statistics and Probability
Issue Date: 15-Jan-2016
Publisher: Nature Publishing Group
Source: Nature Physics, AOP, 2016
Abstract: Real networks often form interacting parts of larger and more complex systems. Examples can be found in different domains, ranging from the Internet to structural and functional brain networks. Here, we show that these multiplex systems are not random combinations of single network layers. Instead, they are organized in specific ways dictated by hidden geometric correlations between the individual layers. We find that these correlations are strong in different real multiplexes, and form a key framework for answering many important questions. Specifically, we show that these geometric correlations facilitate: (i) the definition and detection of multidimensional communities, which are sets of nodes that are simultaneously similar in multiple layers; (ii) accurate trans-layer link prediction, where connections in one layer can be predicted by observing the hidden geometric space of another layer; and (iii) efficient targeted navigation in the multilayer system using only local knowledge, which outperforms navigation in the single layers only if the geometric correlations are sufficiently strong. Our findings uncover fundamental organizing principles behind real multiplexes and can have important applications in diverse domains.
URI: http://ktisis.cut.ac.cy/handle/10488/9004
ISSN: 17452473
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