Structural reducibility of multilayer networks

By Manlio De Domenico, Vincenzo Nicosia, Alex Arenas, Vito Latora. Nature Communications, 2014

628 citations recorded by the source corpus.

Many complex systems can be represented as networks consisting of distinct types of interactions, which can be categorized as links belonging to different layers. For example, a good description of the full protein–protein interactome requires, for some organisms, up to seven distinct network layers, accounting for different genetic and physical interactions, each containing thousands of protein–protein relationships. A fundamental open question is then how many layers are indeed necessary to accurately represent the structure of a multilayered complex system. Here we introduce a method based on quantum theory to reduce the number of layers to a minimum while maximizing the distinguishability between the multilayer network and the corresponding aggregated graph. We validate our approach on synthetic benchmarks and we show that the number of informative layers in some real multilayer networks of protein–genetic interactions, social, economical and transportation systems can be reduced by up to 75%. Multilayer networks have been used to capture the structure of complex systems with different types of interactions, but often contain redundant information. Here, De Domenico et al. present a method based on quantum information, to identify the minimal configuration of layers to retain.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/398c0bf06ee1c63a384daf41f462e3e8a72832f3

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In Molecular Biology

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Citations in the corpus

• Big data in healthcare: management, analysis and future prospects (2019, background): “For example, quantum theory can maximize the distin-guishability between a multilayer network using a minimum number of layers [42].”
• Linear Algebra and its Applications (2010, neutral): “Consider the step with (4.2) from t n to t n + 1 starting in w n and write in short (4.6) We stop after k 1 , k 2 iterations for (cid:4) 1 , (cid:4) 2 approximating w n + 1 by Ideally, (cid:6) w n + 1 − ˆ w n + 1 (cid:6) issmallerthantheunknownlocaltruncationerrorfor w n + 1 whichwerepresent by the…”
• The science of science: from the perspective of complex systems (2017, background): “Issues include the centrality measures [460], community detection [461], structural reducibility [462], and coupled dynamics (e.”
• The multilayer nature of ecological networks (2015, methodology): “ijsr [( Aijs − γs kisdjs ms ) δsr + δijωjsr ] δ(gis, gjr) , (2)”